A comparison of self-reported use of primary mental health care versus provincial administrative health records
Bibliographic record
Abstract
Most mental health (MH) care in Canada is provided by General Practitioners (GPs). Thus. information about use of primary MI-I care is fundamentally important to researchers and policy makers. The two predominant sources of data on primary MN care are self-reported data, and provincial administrative databases which capture physician reimbursement data for medical services provided to virtually all residents, under Canada’s publicly-funded universal health care system. The objective of this research was to compare estimates of primary MI-I care from the two types of data over a 12 month reference period, using an individual-level data linkage in a sample of 2,37X residents of the province of British Columbia. The proportion of individuals in this sample who had MI-I care from a GP was approximately twice as high in the administrative data (19.3%) versus the self-reported data (8.5%). in contrast, the number o/ A4H visits to each individual’s main GP tended to be higher in the self-reported data than the administrative data. In terms of agreement. three-quarters of individuals who had primary MH care according to the administrative data did not report such care. while one-third of individuals who reported primary Mu care did not have an administrative record of such care. The study also explored whether individuals in the sample who had had a major depressive episode (MDE) in the 12 month reference period, were more or less likely than individuals who had not had an MDE to have had primary MI-I care according to both data sources versus only one data source. The results of the study indicated that individuals who had hat/an A/IDE had a greater likelihood of having had care according to both data sources. The self-reported data were from the 2002 Canadian Community I-Health Survey on Mental Health and Well-Being, and the method used to identify primary MI-I care in the administrative data closely paralleled the method that has recently been proposed for national surveillance of MH in Canada. It is hoped that the findings will help researchers, policymakers and methodologists to make informed choices when collecting. analyzing and interpreting each type of data.
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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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".