A scoping review on heterogeneity in rehabilitation research: implications for return to duty in a military population
Bibliographic record
Abstract
Introduction: Understanding population heterogeneity in rehabilitation research is important, since varying conditions can influence clinical outcomes. The objectives of this scoping review were to review rehabilitation studies that used a heterogeneous group in a civilian or military population, and to discuss the impact of heterogeneity on participation outcomes such as return to duty in the Canadian Armed Forces. Methods: Literature search resulted in extraction of 37 articles, which were sorted according to degree of heterogeneity and type of outcomes examined. Results: The largest number of studies pertained to civilians ( n=26), followed by military ( n=10), and Veterans ( n=1). We found various degrees of heterogeneity in population, setting, intervention, and outcome in these studies. Discussion: Studies extracted seemed to show a superior positive outcome in return to work/duty when the group was heterogeneous. Military rehabilitation studies examining return to duty tended to include a highly heterogeneous population. Future studies pertaining to return to work/duty and using a heterogeneous group should include a wide range of outcomes in the domains of the International Classification of Functioning, Health and Disease. Potential economic benefits in using a heterogeneous-based intervention are also discussed, along with implications for the Canadian Armed Forces.
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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 | 0.041 | 0.189 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.022 | 0.023 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".