Typology of patients with mental health disorders and perceived continuity of care
Bibliographic record
Abstract
Background: While multiple socio-demographic, clinical and service use variables have been associated with continuity of care (CoC) in patients diagnosed with mental health disorders (MHDs), little is known about how these variables may inform clinical practice and service planning.Aim: This article identified profiles of patients with MHDs to better understand their perceptions of CoC.Method: The sample for this cross-sectional study comprised 327 patients recruited by staff or self-referred from four local health networks in Quebec (Canada). Data were collected using standardized instruments, and patient medical records. A three-factor conceptual framework based on Andersen’s Behavioral Model was used, integrating predisposing, needs and enabling factors.Results: Cluster analyses identified five patient profiles. Profiles that included relatively more patients with common MHDs reported less continuity than those with patients primarily affected by severe MHDs.Conclusions: Service planning and delivery should be better adapted to patient profiles in order to improve CoC, and increased access to services prioritized.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".