Primary mental health care visits in self-reported data versus provincial administrative records.
Bibliographic record
Abstract
BACKGROUND: Survey data and provincial administrative health data are the major sources of population estimates of mental health care visits to General Practitioners (GPs). Previous research has suggested that self-reported estimates of the number of mental health-related visits per person to health professionals may exceed estimates obtained from physician reimbursement records. DATA AND METHODS: Self-reported data from the 2002 Canadian Community Health Survey (CCHS): Mental Health and Well-being and administrative records from the Medical Services Plan of British Columbia were linked. The analytic sample consisted of 145 CCHS respondents who had at least one mental health visit to a GP in the past 12 months according to both data sources. High Reporters (self-reported visits exceeded number in administrative data), Low Reporters (self-reported visits were less than number in administrative data), and Exact Matches were analyzed in two ways. The first analysis used diagnostic codes to identify mental health-related visits in the administrative data. For the second analysis, all GP visits in the administrative data were counted as "possibly" mental health-related. Differences were described based on the median number of visits. RESULTS: When diagnostic codes were used to identify mental-health-related visitis in the administrative data, High Reporters (49%) substantially exceeded Low Reporters (24%). The remaining 27% were Exact Matches. Based on a broader definition of a mental health visit, 51% were Exact Matches. High reporting was common among people with mental disorders. INTERPRETATION: Self-reported data and administrative data provide different estimates of the number of mental health visits per person to GPs. The discrepancy can be large.
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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 | 0.020 | 0.117 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".