Conceptual review of measuring functional impairment: findings from the Weiss Functional Impairment Rating Scale
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".