The VHA Interprofessional Collaboration Competency Framework for Home Healthcare: Development and Implementation Requirements in Ontario, Canada
Bibliographic record
Abstract
There is a heightened focus on the need for interprofessional collaboration (IPC) to improve person-centered care. The unique challenges of the home healthcare context are not reflected in existing frameworks. The purpose of this work was to develop an interprofessional competency framework for home healthcare and to identify enablers and barriers to implementation. The framework was co-designed with home healthcare clients, providers and leaders through a 5-step process: a rapid literature review, “Design Day,” iterative co-design, shareback meetings, and refinement. Seven competencies were identified around the core value of patient-centered care, supported by 4 guiding principles. The framework was designed to promote individual-, team- and organization-level competency through identifying required elements at each level. Three months after clinical leaders were introduced to the IPCC Framework, 38 participated in workshops in which they provided insights on enablers and barriers to implementation. These were analyzed thematically with reference to the COM-B theory of change model. Major themes reflected capabilities to implement practice changes, opportunities enabling the integration of IPC, barriers to implementing IPC, and motivation to practice interprofessionally. Most identified barriers existed structurally at the team and organizational levels, limiting or disincentivizing interprofessional collaboration. This framework provides an approach that home healthcare organizations can use to guide the advancement of IPC, identifying how competencies may be enacted at the individual, team, and organizational levels within the relatively autonomous and geographically disparate context of home healthcare.
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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 | 0.021 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.004 |
| 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".