Analysis of Complaints to a Tertiary Care Pain Clinic Over a Nine‐Year Period
Bibliographic record
Abstract
BACKGROUND: The present study is the result of an internal audit and examines the profiles of complainants and the sources and nature of complaints toward the staff in a tertiary care pain clinic, the Comprehensive Pain Program of the Toronto Western Hospital in Toronto, Ontario. METHODS: All sources of complaints over a nine-year period were reviewed, which included the following: Toronto Western Hospital Patient Relations (PR) records, with a subset of the files qualitatively analyzed in depth regarding the nature of complaints and complainants; complaints that bypassed PR and were addressed directly to the program director against members of the staff; complaints to the College of Physicians and Surgeons of Ontario; and complaints recorded anonymously at rateMDs.com. RESULTS: Although the prevalence of PR complaints was very low (1.73 complaints per 1000 visits), several other sources of complaints were identified. The typical complainant was a Canadian-born woman acting on her behalf or on behalf of a family member. More than one-half of the complaints were directed against the physicians regarding their opinion of psychological factors augmenting the patient's presentation and/or inappropriate use of opioids. Defensive techniques instituted by the Comprehensive Pain Program staff in reaction to the complaints are discussed, and pertinent literature is reviewed. CONCLUSION: The present study is the first to examine the nature of complaints and complainants from a Canadian pain clinic. Further studies are needed to explore the complex issues of patient and staff interactions, and complaints in the era of 'patient-centred care'.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".