Evaluating the measurement of mental health service accessibility, acceptability, and availability in the Canadian Community Health Survey.
Bibliographic record
Abstract
Given the underutilization of mental health services by those with mental health problems, growing attention has focused on barriers to utilizing services. Several researchers have used the Canadian Community Health Survey (CCHS) cycle 1.2 dataset, including measures of barriers because of accessibility, acceptability, and availability, to explore the gap between mental health service need and use. Because the psychometric properties of these barrier measures have not been evaluated, the reliability and validity of the 3 measures were examined in the present study. Confirmatory factor analyses were conducted using data from CCHS participants who had indicated unmet need regarding information on mental illness and its treatments; availability of services, medication, and psychotherapy or counseling (n = 353); as well as the full sample of participants reporting any unmet need in the past year (n = 1,784). The hypothesized 3-factor model (i.e., accessibility, acceptability, and availability) failed to converge with both samples. Exploratory factor analysis was conducted using data from the full sample (n = 1,784), and a possible 2-factor solution was obtained. Reliability analyses on this 2-factor model, as well as the 3-factor model included in the CCHS, demonstrated that internal consistency values failed to attain acceptable levels of reliability (i.e., α < .70). Based on these analyses, these barrier measures are neither reliable nor valid. These measures should not be used to examine barriers to service within the CCHS 1.2 dataset, and caution should be exercised in interpreting the findings of studies that used these measures.
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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 | 0.030 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".