A survey of Ontario chiropractors: their views on maximizing patient compliance to prescribed home exercise.
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to compile an inventory of the strategies most frequently used by Ontario chiropractors in their efforts to maximize patient compliance to prescribed home exercise. DESIGN: The design consisted of a cross-sectional self-report web-based survey of Ontario chiropractors. PARTICIPANTS: Eligible participants consisted of chiropractors in active practice in Ontario (treating, on average, at least 1 patient per week) and prescribing home exercises at least once in the last 30 days. RESULTS: The compliance strategies used most frequently by Ontario chiropractors were: keeping instructions simple (82%, 95% CI = 75-90%); motivating patients by explaining exercises in a positive and enthusiastic manner (81%, 95% CI = 74-89%); giving patients encouragement, support and praise (80%, 95% CI = 72-88%); prescribing exercises that require low-cost equipment (70%, 95% CI = 61-78%); and supplying patients with material that helps demonstrate the exercises (62%, 95% CI = 53-71%) and educating patients by discussing the importance of and benefits to exercise (62%, 95% CI = 53-71%). CONCLUSION: There appeared to be respondent consensus on the main compliance strategies used by Ontario chiropractors. Now that we have a current listing.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".