Bibliographic record
Abstract
OBJECTIVE: Most people with depression do not receive treatment, even though effective interventions are available. Population-based data can assist health service planners to improve access to mental health services. This study aimed to examine the determinants of untreated depression in Canada's Atlantic provinces. METHOD: This study used data from the Canadian Community Health Survey Cycle 1.1. Logistic regression models explored the prevalence of depression and associated patterns of mental health service use among population subgroups. RESULTS: Of the respondents, 7.3% experienced major depression in the previous year, as measured by the Composite International Diagnostic Interview Short Form. Individuals with the following characteristics were at increased risk for depression: female sex; widowed, separated, or divorced marital status; low income; and 2 or more comorbid medical conditions. Only 40% of respondents with probable depression reported any consultation about their condition with a general practitioner or mental health specialist. Less than one-quarter of Atlantic Canadians with depression reported receiving levels of care consistent with practice guidelines. Vulnerable groups, including older individuals, people with low levels of education, and those living in rural areas, were significantly less likely to receive treatment in either primary or specialty care. CONCLUSIONS: These findings suggest inequitable access to services and the need to target interventions to at-risk populations by raising awareness among the public and health care providers. Health systems in the Atlantic region must work toward achieving consistent longitudinal care for a larger proportion of individuals suffering from depression by studying the underlying factors for service use among underserved groups.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".