Developing a Framework of Integrated Competencies for Adaptive Expertise in Integrated Physical and Mental Health Care
Bibliographic record
Abstract
Phenomenon: Despite the emergence of the integrated care (IC) model, IC is variably taught and is challenged by current siloed competency domains. This study aimed to define IC competencies spanning multiple competency domains. Approach: Iterative facilitated discussions were conducted at a half-day education retreat with 25 key informants including clinician educators and education scientists. Seven one-on-one semistructured interviews were subsequently conducted with different interprofessional providers in IC settings within a Canadian context. Data collection grounded in patient cases with a physical illness and concurrent mental illness (medical psychiatry) were used to elicit identification of complex patient needs and the key medical psychiatry knowledge and skills required to address these needs. A thematic analysis of transcripts was performed using constant comparison to iteratively identify themes. Findings: Participants described 4 broad competency domains necessary for expertise in IC: (a) extensive integrated knowledge of biopsychosocial aspects of disease, systems of care, and social determinants of care; (b) skills to establish a longitudinal alliance with the patient and functional relationships with colleagues; (c) constructing a comprehensive understanding of individual patients’ complex needs and how these can be met within their health and social systems; and (d) the ability to effectively meet the patient’s needs using IC models. These 4 domains were linked by an overarching philosophy of care encompassing key enabling attitudes such as proactively pursuing depth to understand patient and system complexity while maintaining a patient-centered approach. Insights: The study addresses how development of IC expertise can be fostered by integration of individual IC competency domains. The findings align with previous research suggesting that competencies from existing frameworks are being enacted jointly in expert capabilities to meet the complex needs of patients, in this case with comorbid physical and mental health concerns.
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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 | 0.013 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.030 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".