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Record W2896312564 · doi:10.1136/ebmental-2018-300025

Conceptual review of measuring functional impairment: findings from the Weiss Functional Impairment Rating Scale

2018· review· en· W2896312564 on OpenAlexaff
Margaret D. Weiss, Nicole M. McBride, Stephanie G. Craig, Peter S. Jensen

Bibliographic record

VenueEvidence-Based Mental Health · 2018
Typereview
Languageen
FieldMedicine
TopicPsychosomatic Disorders and Their Treatments
Canadian institutionsYork University
Fundersnot available
KeywordsFunctional impairmentRating scaleClinical psychologyPsychologyPsychological interventionMEDLINEScale (ratio)Clinical trialOutcome (game theory)Inclusion and exclusion criteriaMedicinePsychiatryDevelopmental psychologyAlternative medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: This is a narrative review of validation and outcome studies using the Weiss Functional Impairment Rating Scale (WFIRS). The objective of the review is to establish a framework for understanding functional impairment and create a definition for functional response and remission. METHODS: We conducted a literature search via MEDLINE, EBSCO and Google Scholar with no date restrictions and reviewed bibliographies of selected publications. Publications found in languages other than English were translated and clarification obtained from the author(s) if needed. Inclusion criteria were any manuscript that was either a WFIRS psychometric validation study or a clinical trial using the WFIRS as an outcome. There were no exclusion criteria. RESULTS: The WFIRS has been validated in multiple cultures, and in clinical, research and control populations. The WFIRS has robust psychometric properties across ages, psychiatric status and informants. Outcome studies show variable improvement, with different response patterns between domains and among different interventions. CONCLUSION: Symptom improvement and remission needs to be complemented with evaluation of functional improvement and remission to obtain a full picture of clinical status over the course of treatment.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.283
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.142
GPT teacher head0.366
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations101
Published2018
Admission routes1
Has abstractyes

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