Quality of care for major depression and its determinants: a multilevel analysis
Bibliographic record
Abstract
BACKGROUND: Numerous studies highlight an important gap in the quality of care for depression in primary care. However, basic indicators were often used. Few of these studies examined factors associated with receiving adequate treatment, particularly with a simultaneous consideration of individual and organizational characteristics. The purpose of this study was to estimate the proportion of primary care patients with a major depressive episode (MDE) who receive adequate treatment and to examine the individual and organizational (i.e., clinic-level) characteristics associated with the receipt of at least one minimally adequate treatment for depression. METHODS: The sample used for this study included 915 adults consulting a general practitioner (GP), regardless of the motive of consultation, meeting DSM-IV criteria for MDE during the 12 months preceding the survey (T1), and nested within 65 primary care clinics. Data reported in this study were obtained from the "Dialogue" project. Adherence rates for 27 quality indicators selected to cover the most important components of depression treatment were estimated. Multilevel analyses were conducted. RESULTS: Adherence to guidelines was high (>75%) for one third of the quality indicators that were measured but was low (<60%) for nearly half of the measures. Just over half of the sample (52.2%) received at least one minimally adequate treatment for depression. At the individual level, determinants of receipt of minimally adequate care included age, having a family physician, a supplementary insurance coverage, a comorbid anxiety disorder and the severity of depression. At the clinic level, determinants included the availability of psychotherapy on-site, the use of treatment algorithms, and the mode of remuneration. CONCLUSIONS: Our findings suggest that interventions are needed to increase the extent to which primary mental health care conforms to evidence-based recommendations. These interventions should target specific populations (i.e. the younger adults and the elderly), enhance accessibility to psychotherapy and to a regular family physician, and support primary care physicians in their clinical practice with patients suffering from depression in different ways such as developing knowledge to treat depression and adapting mode of remuneration.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.006 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".