Perceived effects of the delisting of chiropractic services from the Ontario Health Insurance Plan on practice activities: a survey of chiropractors in Toronto, Ontario.
Bibliographic record
Abstract
UNLABELLED: The purpose of this study was to survey a random sample of Toronto chiropractors and gather their perceptions of the effects that the delisting of chiropractic services from OHIP had on their practices profiles. METHODS: A survey was mailed to 199 chiropractors who were asked to disclose demographic information, if they were in practice at the time when OHIP coverage was in effect, the perceived effect OHIP delisting had on their patient volumes, income, the profession's credibility and if they would be in favor of having OHIP reinstated. RESULTS: Among the 123 respondents in practice during OHIP coverage (n = 92), 48.9% indicated they perceived their practice income and 36.6% perceived their patient volume was negatively affected; 57.5% reported both had subsequently recovered. Almost 50% perceived OHIP delisting negatively affected the profession's credibility and 46.1% of respondents were in favor of it being reinstated for chiropractic services; this percentage was much higher among chiropractors who were not in practice during the time of OHIP coverage. CONCLUSION: Most chiropractors reported that patient volumes and incomes have returned to pre-delisting levels and few chiropractors who were in practice during OHIP coverage expressed interest in having it reinstated.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".