Developing a coding taxonomy to analyze dental regulatory complaints
Bibliographic record
Abstract
BACKGROUND: As part of their mandate to protect the public, dental regulatory authorities (DRA) in Canada are responsible for investigating complaints made by members of the public. To gain an understanding of the nature of and trends in complaints made to the Royal College of Dental Surgeons of Ontario (RCDSO), Canada's largest DRA, a coding taxonomy was developed for systematic analysis of complaints. METHODS: The taxonomy was developed through a two-pronged approach. First, the research team searched for existing complaints frameworks and integrated data from a variety of sources to ensure applicability to the dental context in terms of the generated items/complaint codes in the taxonomy. Second, an anonymized sample of complaint letters made by the public to the RCDSO (n = 174) were used to refine the taxonomy. This sample was further used to assess the feasibility of use in a larger content analysis of complaints. Inter-coder reliability was also assessed using a separate sample of letters (n = 110). RESULTS: The resulting taxonomy comprised three domains (Clinical Care and Treatment, Management and Access, and Relationships and Conduct), with seven categories, 23 sub-categories, and over 100 complaint codes. Pilot testing for the feasibility and applicability of the taxonomy's use for a systematic analysis of complaints proved successful. CONCLUSIONS: The resulting coding taxonomy allows for reliable documentation and interpretation of complaints made to a DRA in Canada and potentially other jurisdictions, such that the nature of and trends in complaints can be identified, monitored and used in quality assurance and improvement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.005 |
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; both teacher heads agree on what is shown here.
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".