Tag: pubmed

Adv Skin Wound Care. 2024 Jul 17. doi: 10.1097/ASW.0000000000000177. Online ahead of print.

ABSTRACT

BACKGROUND: High bacterial burden stalls wound healing and can quickly progress to infection and sepsis in complex, older-adult patients in long-term care (LTC) or skilled nursing facilities (SNFs).

OBJECTIVE: To investigate the outcomes of point-of-care fluorescence (FL) imaging (MolecuLight i:X) of bacterial loads, which are frequently asymptomatic, to inform customized wound treatment plans for patients in LTC/SNFs.

METHODS: In this retrospective pre/postinterventional cohort study, the authors compared the healing and infection-associated outcomes of 167 pressure injuries from 100 Medicare beneficiaries before and after implementation of FL imaging.

RESULTS: Most patient demographics and wound characteristics did not differ significantly between the standard-of-care (SOC; n = 71 wounds) and FL (n = 96 wounds) cohorts. Significantly more wounds (+71.0%) healed by 12 weeks in the FL cohort (38.5%) versus the SoC cohort (22.5%). Wounds in the FL cohort also healed 27.7% faster (-4.8 weeks), on average, and were 1.4 times more likely to heal per Kaplan-Meier survival analysis (hazard ratio = 1.40; 95% CI, 0.90-2.12). Infection-related complications decreased by 75.3% in the FL cohort, and a significant shift from largely systemic to topical antibiotic prescribing was evidenced.

CONCLUSIONS: Fluorescence-imaging-guided management of wounds significantly improved healing and infection outcomes in highly complex and multimorbid patients in LTC/SNFs. Proactive bacterial infection management via local treatments was enabled by earlier, objective detection. These reported outcome improvements are comparable to randomized controlled trials and cohort studies from less compromised, selectively controlled outpatient populations. Fluorescence imaging supports proactive monitoring and management of planktonic and biofilm-encased bacteria, improving patient care in a complex, real-world setting.

PMID:39023985 | DOI:10.1097/ASW.0000000000000177

J Consult Clin Psychol. 2024 Jun;92(6):327-329. doi: 10.1037/ccp0000897.

ABSTRACT

Treatment personalization has evolved into an important zeitgeist in psychotherapy research. To date, such efforts have principally embodied a unidirectional focus on personalizing interventions to the patient. For example, earlier work in this area attempted to determine whether, on average, certain patients with certain characteristics or needs would respond better to one treatment package versus others. To the extent such aggregate “Aptitude × Treatment interactions” emerged, they could help guide overarching treatment selection. More recently, and drawing on technological and statistical advancements (e.g., machine learning, dynamic modeling), predictive algorithms can help determine for which individual patients certain treatment packages (DeRubeis et al., 2014) or specific during-session interventions within them (Fisher & Boswell, 2016) confer the most advantage for clinical improvement. Again, such work can help guide treatment decisions, though now at multiple care points. Although the aforementioned innovations in personalized psychotherapy have been leading-edge, precision care need not remain unidirectional. Rather, it can be complemented by efforts to personalize treatment decisions to the therapist. Namely, we can harness therapist effectiveness data to help ensure that therapists treat the patients they are empirically most equipped to help and use the interventions with which they have had the most empirical success. Such threads have been the focus of our team’s novel, evolving, and multimethod work on improving psychotherapy by leveraging therapists’ own practice-based evidence. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

PMID:39023981 | DOI:10.1037/ccp0000897

Psychol Methods. 2024 Jul 18. doi: 10.1037/met0000681. Online ahead of print.

ABSTRACT

The coefficient of determination, R², also called the explained variance, is often taken as a proportional measure of the relative determination of model on outcome. However, while R² has some attractive statistical properties, its reliance on squared variations (variances) may limit its use as an easily interpretable descriptive statistic of that determination. Here, the properties of this coefficient on the squared scale are discussed and generalized to three relative measures on the original scale. These generalizations can all be expressed as transformations of R², and alternatives can therefore also be calculated by plugging in related estimates, such as the adjusted R². The third coefficient, new for this article, and here termed the CoD_SD (the coefficient of determination in terms of standard deviations), or R_π (R-pi), equals R²/(R²+1-R²). It is argued that this coefficient most usefully captures the relative determination of the model. When the contribution of the error is c times that of the model, the CoD_SD equals 1/(1 + c), while R² equals 1/(1 + c²). (PsycInfo Database Record (c) 2024 APA, all rights reserved).

PMID:39023979 | DOI:10.1037/met0000681

Psychol Methods. 2024 Jul 18. doi: 10.1037/met0000651. Online ahead of print.

ABSTRACT

Psychologists tend to rely on verbal descriptions of the environment over time, using terms like “unpredictable,” “variable,” and “unstable.” These terms are often open to different interpretations. This ambiguity blurs the match between constructs and measures, which creates confusion and inconsistency across studies. To better characterize the environment, the field needs a shared framework that organizes descriptions of the environment over time in clear terms: as statistical definitions. Here, we first present such a framework, drawing on theory developed in other disciplines, such as biology, anthropology, ecology, and economics. Then we apply our framework by quantifying “unpredictability” in a publicly available, longitudinal data set of crime rates in New York City (NYC) across 15 years. This case study shows that the correlations between different “unpredictability statistics” across regions are only moderate. This means that regions within NYC rank differently on unpredictability depending on which definition is used and at which spatial scale the statistics are computed. Additionally, we explore associations between unpredictability statistics and measures of unemployment, poverty, and educational attainment derived from publicly available NYC survey data. In our case study, these measures are associated with mean levels in crime rates but hardly with unpredictability in crime rates. Our case study illustrates the merits of using a formal framework for disentangling different properties of the environment. To facilitate the use of our framework, we provide a friendly, step-by-step guide for identifying the structure of the environment in repeated measures data sets. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

PMID:39023977 | DOI:10.1037/met0000651

J Exp Psychol Learn Mem Cogn. 2024 Jul 18. doi: 10.1037/xlm0001338. Online ahead of print.

ABSTRACT

Accurate quantification of effect sizes has the power to motivate theory and reduce misinvestment of scientific resources by informing power calculations during study planning. However, a combination of publication bias and small sample sizes (∼N = 25) hampers certainty in current effect size estimates. We sought to determine the extent to which sample sizes may produce errors in effect size estimates for four commonly used paradigms assessing attention, executive function, and implicit learning (attentional blink, multitasking, contextual cueing, and serial response task). We combined a large data set with a bootstrapping approach to simulate 1,000 experiments across a range of N (13-313). Beyond quantifying the effect size and statistical power that can be anticipated for each study design, we demonstrate that experiments with lower N may double or triple information loss. We also show that basing power calculations on effect sizes from similar studies yields a problematically imprecise estimate between 40% and 67% of the time, given commonly used sample sizes. Last, we show that skewness of intersubject behavioral effects may serve as a predictor of an erroneous estimate. We conclude with practical recommendations for researchers and demonstrate how our simulation approach can yield theoretical insights that are not readily achieved by other methods such as identifying the information gained from rejecting the null hypothesis and quantifying the contribution of individual variation to error in effect size estimates. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

PMID:39023976 | DOI:10.1037/xlm0001338

Rehabil Psychol. 2024 Jul 18. doi: 10.1037/rep0000562. Online ahead of print.

ABSTRACT

OBJECTIVE: This study investigated the predictive value of illness and treatment beliefs for patient satisfaction and health-related quality of life (HRQOL) in adolescents receiving inpatient rehabilitation treatment. In addition, we examined the relationship between fulfilled rehabilitation-related treatment expectations and patient satisfaction.

METHOD: In this longitudinal study (recruitment between April 2019 and March 2020), 170 participants (M = 14.3 years [SD = 1.6]) answered self-report questionnaires before and at the end of rehabilitation (6 weeks later). We applied multiple hierarchical regression analyses, controlling for sociodemographic and diagnoses variables.

RESULTS: The results showed fulfilled expectations of treatment success and sustainability to be a significant predictor of patient satisfaction (p < .01). The illness belief dimension of emotional representation predicted HRQOL (p < .01). Rehabilitation-related treatment beliefs were not predictive of any outcome.

CONCLUSION: This study provides a first insight into the relationships between these constructs in the context of inpatient pediatric rehabilitation. However, future research is needed to further examine illness and treatment beliefs in this specific treatment setting. Practical implications concern the incorporation of children’s and adolescents’ beliefs into treatment management to optimize rehabilitation outcomes. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

PMID:39023956 | DOI:10.1037/rep0000562

Psychol Trauma. 2024 Jul 18. doi: 10.1037/tra0001726. Online ahead of print.

ABSTRACT

OBJECTIVE: The present study explored rates of burnout and racial trauma among 182 Black mental health professionals (BMHPs) and utilized racial-cultural theory to explore potential protective factors against burnout and racial trauma.

METHOD: We collected data from 182 Black psychologists and counselors who were active mental health professionals during 2020. Descriptive statistics, multivariate analyses of variance, follow-up univariate analyses of variance, bivariate correlations, and multiple regression analyses were used.

RESULTS: Both burnout and racial trauma were considerably higher among BMHPs than has been reported across general samples of helping professionals and across a sample of Black participants across the United States. Differences among rates of burnout and racial trauma existed across genders and specialties (i.e., counseling and psychology). Higher levels of social support and an external locus of control significantly predicted lower levels of burnout and racial trauma. In addition, higher levels of resilient coping predicted lower levels of burnout. Last, more frequent meetings with a mentor significantly predicted lower levels of racial trauma.

CONCLUSIONS: Results from this study suggest that BMHPs may be more susceptible to burnout and race-based traumatic stress as a result of their work. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

PMID:39023947 | DOI:10.1037/tra0001726

Psychol Trauma. 2024 Jul 18. doi: 10.1037/tra0001728. Online ahead of print.

ABSTRACT

OBJECTIVE: The City Birth Trauma Scale (City BiTS) assesses postpartum posttraumatic stress disorder (PTSD) based on the Diagnostic and Statistical Manual of Mental Disorders (5th ed.) criteria. Although it has been validated worldwide, predictive validity has not been previously examined. Moreover, no Italian version of the scale exists. This study aimed to test the bifactor latent structure and alternative models, internal consistency, test-retest reliability, convergent validity, divergent validity, and predictive validity of the City BiTS.

METHOD: Women (N = 629) who had given birth within the past 3 months completed an online survey including sociodemographic and obstetric characteristics, the City BiTS, the Impact of Event Scale-Revised, and the Edinburgh Postnatal Depression Scale. After 3 months, women completed the City BiTS again and reported their intention to breastfeed during the 1-year postpartum.

RESULTS: Exploratory factor analysis confirmed the two-factorial structure. In confirmatory factor analysis, the two-factorial solution showed the best model fit. Internal consistency was good to excellent for the subscales and the total scale. Correlation analyses showed strong convergent validity with the Impact of Event Scale-Revised, high divergent validity with the Edinburgh Postnatal Depression Scale, high test-retest reliability, and good predictive validity with the intention to exclusively breastfeed. Moreover, the Birth-Related Symptoms subscale distinguished between different types of delivery.

CONCLUSIONS: The City BiTS-Italian is the first measure evaluating and diagnosing childbirth-related PTSD symptoms based on Diagnostic and Statistical Manual of Mental Disorders (5th ed.) in Italy. The factorial structure and validity reported in other cultural contexts were confirmed; moreover, findings add evidence to the scale’s temporal stability and predictive validity. Besides contributing to clinical purposes, the City BiTS-Italian will facilitate international comparability regarding the prevalence of PTSD following childbirth. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

PMID:39023943 | DOI:10.1037/tra0001728

Psychol Rev. 2024 Jul 18. doi: 10.1037/rev0000479. Online ahead of print.

ABSTRACT

The explanation of psychological phenomena is a central aim of psychological science. However, the nature of explanation and the processes by which we evaluate whether a theory explains a phenomenon are often unclear. Consequently, it is often unknown whether a given psychological theory indeed explains a phenomenon. We address this shortcoming by proposing a productive account of explanation: a theory explains a phenomenon to some degree if and only if a formal model of the theory produces the statistical pattern representing the phenomenon. Using this account, we outline a workable methodology of explanation: (a) explicating a verbal theory into a formal model, (b) representing phenomena as statistical patterns in data, and (c) assessing whether the formal model produces these statistical patterns. In addition, we provide three major criteria for evaluating the goodness of an explanation (precision, robustness, and empirical relevance), and examine some cases of explanatory breakdowns. Finally, we situate our framework within existing theories of explanation from philosophy of science and discuss how our approach contributes to constructing and developing better psychological theories. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

PMID:39023936 | DOI:10.1037/rev0000479

Chaos. 2024 Jul 1;34(7):073138. doi: 10.1063/5.0206406.

ABSTRACT

Transformations are a key tool in the qualitative study of dynamical systems: transformations to a normal form, for example, underpin the study of instabilities and bifurcations. In this work, we test, and when possible establish, an equivalence between two different artificial neural networks by attempting to construct a data-driven transformation between them, using diffusion maps with a Mahalanobis-like metric. If the construction succeeds, the two networks can be thought of as belonging to the same equivalence class. We first discuss transformation functions between only the outputs of the two networks; we then also consider transformations that take into account outputs (activations) of a number of internal neurons from each network. Whitney’s theorem dictates the number of (generic) measurements from one of the networks required to reconstruct each and every feature of the second network. The construction of the transformation function relies on a consistent, intrinsic representation of the network input space. We illustrate our algorithm by matching neural network pairs trained to learn (a) observations of scalar functions, (b) observations of two-dimensional vector fields, and (c) representations of images of a moving three-dimensional object (a rotating horse). We also demonstrate reconstruction of a network’s input (and output) from minimal partial observations of intermediate neuron activations. The construction of equivalences across different network instantiations clearly relates to transfer learning and will also be valuable in establishing equivalence between different machine learning-based tools.

PMID:39023924 | DOI:10.1063/5.0206406