The Predictive Power of Narrative Data in Occupational Therapy Evaluation
Bibliographic record
Abstract
OBJECTIVE: This study examined whether adding the Canadian Occupational Performance Measure (COPM) to existing occupational therapy evaluation measures used in a subacute skilled nursing facility unit enhanced the accuracy of therapists' predictions of the functional status of clients at discharge. METHOD: This study utilized a prospective comparison design. Two independent predictive variables were developed using the standard Functional Independent Measure (FIM), and an enhanced FIM that included narrative information from the Canadian Occupational Performance Measure (FIM/COPM). These variables were subsequently compared with the actual FIM discharge (DFIM) scores for 31 clients. The primary author (D.S.) gathered data from chart review and conducted the statistical analysis. The data were analyzed using descriptive correlations (Pearson r) and comparison statistics (Wilcoxon signed rank test). RESULTS: Comparison statistics (Wilcoxon signed rank test) revealed a statistically significant difference between the standard FIM predictive score and the discharge FIM score. No statistically significant difference was found between the FIM/COPM predictive score and the discharge FIM score. These findings suggest that predictive scores based solely on information attained from the standard FIM resulted in less accuracy in outcome predictions. Correlational analyses further supported these conclusions. CONCLUSION: The findings support the study hypothesis that use of the COPM in combination with the FIM enhances accuracy in prediction of outcomes for rehabilitative services for persons in adult physical disabilities settings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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 teacher head, 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".