Predictors of Number of Healthcare Professionals Consulted by Individuals with Mental Disorders or High Psychological Distress
Bibliographic record
Abstract
This study assesses the contribution of predisposing, enabling, and needs factors and related variables that predicted the number of healthcare professionals consulted for mental health reasons among 746 individuals with mental disorders and high psychological distress. The data were drawn from the third (T3) and fourth data collection periods (T4) of a longitudinal study conducted in a Quebec/Canada epidemiological catchment area. Hierarchical linear regression was performed on the number of types of healthcare professionals consulted in the 12 months prior to T4. Predictors were identified at T3, classified as predisposing, enabling, and needs factors (i.e., clinical and related variables) according to the Andersen Behavioral Model. Three needs factors were associated with the number of types of healthcare professionals consulted: Post-traumatic stress disorder, stressful events, and marginally suicide ideation. Three enabling factors: Having a family physician, previous use of mental health services, and employment status were also related to the dependent variable. Poor self-perception of mental health status was the only predisposing factor retained. While needs factors were the main predictors of the number of types of healthcare professionals consulted, enabling factors may reduce the influence of needs factors, by the deployment of various strategies that facilitate continuous and appropriate care.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".