Direct and indirect barriers to hypothetical access to care among Canadian forces health services personnel
Bibliographic record
Abstract
BACKGROUND: Though research among Canadian Forces Health Services (CFHS) personnel is limited, the literature suggests formal healthcare is underused. Though much research has been conducted on particular barriers (e.g., stigma), examining a breadth of barriers could better inform behavioral interventions. Furthermore, work has yet to examine the indirect effects of barriers through their impact on intentions to access care. METHODS: CFHS participants were randomly assigned to complete either a mental health (N = 503) or physical health (N = 530) version of the survey. The survey included questions on the perceived impact of barriers, health-related information (e.g., past access to care), intention to seek care, and two hypothetical scenarios (i.e., pneumonia and back injury or post-traumatic stress disorder and depression) as a proxy of access to care. Multiple regressions using Hayes PROCESS macro were conducted to assess the direct and indirect effects (through intentions) of the barriers on hypothetical access to care. RESULTS: Results show conflict with career goals barriers were indirectly linked to all health outcomes, and directly linked to mental health outcomes. Treatment preference barriers were directly and indirectly linked to care seeking only for mental health, while resource barriers were directly linked to care seeking only for physical health. Knowledge and ability to access care barriers were directly linked to care seeking for depression and pneumonia. IMPLICATIONS: Interventions to improve treatment-seeking should be developed only after the behavioural antecedents are understood, and should focus on combining evidence-based techniques to simultaneously target multiple aspects of the behaviour.
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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 | 0.007 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".