Self‐reported use of mental health services versus administrative records: care to recall?
Bibliographic record
Abstract
Estimates of the level of unmet need for mental health treatment often rely on self-reported use of mental health services. However, depressed persons may over-report their use in relation to administrative records if they are highly distressed. This study seeks to replicate and explicate the finding that persons at a high level of distress report more mental health service use than recorded in their healthcare records. The study sample, N = 36,892, 12 years and older, was drawn from the 1996/97 Ontario portion of the Canadian National Population Health Survey. Respondents were individually linked to their administrative mental healthcare records 12 months backward in time. Of these, 96.5% agreed to the link and 23,063 (62.5%) were linked. Almost two-thirds of those who were depressed in the past year were currently at a high level of distress. Differential reporting of use for highly distressed persons in excess of 100% remained in the use of different types of physician providers after adjustments for other potential determinants of use. Telescoping was also not an explanation. The patterns of differential reporting between groups expected to diverge and converge in their recall ability were consistent with a recall bias. As this study was not able to rule out a recall bias, it further accentuates concerns about the impact of bias in the measurement of mental health-service use and inferences made concerning the determinants of use.
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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 | 0.177 | 0.493 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".