Psychologists in integrated primary care at a British Columbian university : understanding interest holder perspectives
Bibliographic record
Abstract
The health of Canadian adults continues to decline following the COVID-19 pandemic. Mental, behavioural and physical health are each inextricably linked, yet clinical psychologists, experts in treating the intersectionality of biopsychosocial determinants to health, are largely siloed from public healthcare. Integrated primary care (IPC) is an evidence-based model of care (vastly underutilized in Canada) where teams of health professionals work together, collaboratively. Using validated scales, this research cross-sectionally assessed a university student health clinic’s interest holders’ (N = 8) attitudes (Mdn = 4.95, IQR = 0.78) and interest levels (Mdn = 5.25, IQR = 1.74) in implementing IPC with a psychologist on a 6-point Likert scale, as well as readiness for change in consultation and practice management (Mdn = 4.00, IQR = 0.63) and intervention and knowledge (Mdn = 4.00, IQR = 0.44) on a 5-point Likert scale, as a registered clinical psychologist joined their team. Median values across measures, ranged from 80 to 88% of the maximum total scales’ score, which likely related to the ongoing success of implementing IPC. Values did not differ significantly between health clinic staff, categorized by groups of either healthcare providers or other staff (p >.05). Thematic analysis found participants’ (N = 7) perceived benefits of doing IPC with a psychologist included (1) the large demand for psychologists in primary care, (2) that IPC was highly feasible through teamwork, (3) it improved individualized primary care, and (4) there was better access to psychological services. Perceived challenges were (1) policy and model-level barriers, with the largest tangible issue being the lack of funding for ongoing IPC with psychologists, and (2) clinic-level constraints, including that participants were still learning the psychologist’s breadth of scope and the model of care. Limitations of the study included the small sample size, whereas a strength was the relatively novel implementation of IPC with a psychologist. Importantly, this small sample analysis will inform next steps in community-wide access to IPC with psychologists across the province of British Columbia, Canada.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.010 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.006 |
| 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".