Enrollment, adherence and retention rates among musculoskeletal disorders rehabilitation practitioners in knowledge translation studies: a systematic review and meta-regression
Bibliographic record
Abstract
BACKGROUND: Practitioners' enrollment, adherence, and retention rates influence estimates of effectiveness in knowledge translation (KT) studies and remain important concerns for implementation researchers. This review aimed to systematically summarize the current evidence on feasibility measures as gauged by enrollment, adherence, and retention rates in KT evaluation studies targeting rehabilitation practitioners treating musculoskeletal disorders (MSDs). METHODS: We searched five electronic databases from the inception to October 2022. We included KT studies that 1) had designs recommended by the Effective Practice and Organisation of Care, 2) targeted rehabilitation practitioners managing patients with MSDs, 3) delivered KT interventions according to the Expert Recommendations for Implementing Change classification, and 4) reported on the feasibility measures (e.g., enrollment, adherence, and retention). Descriptive statistics were conducted to report on study-, practitioners- and intervention-related factors influencing enrollment, adherence, and retention rates. Meta-regression weighted by the sample size of included studies was used to estimate the effect of factors on overall enrollment, adherence, and retention rates. RESULTS: Findings from 33 KT studies reported weighted enrolment, adherence, and retention rate of 82% (range: 32%-100%), 74% (range: 44%-100%), and 65% (range: 36%-100%) respectively for both intervention and control groups. Factors positively influencing enrollment, adherence, and retention rates included designing short study period with short duration intervention. CONCLUSIONS: Intense (e.g., high frequency, short duration) single KT intervention was more appealing for practitioners. Future evaluation studies should clearly report follow-up data, and practitioners' prior training, Results may not apply to non-MSD healthcare providers.
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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 | 0.084 | 0.169 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.048 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".