Interprofessional partnerships in chronic illness care: a conceptual model for measuring partnership effectiveness
Bibliographic record
Abstract
INTRODUCTION: Interprofessional health and social service partnerships (IHSSP) are internationally acknowledged as integral for comprehensive chronic illness care. However, the evidence-base for partnership effectiveness is lacking. This paper aims to clarify partnership measurement issues, conceptualize IHSSP at the front-line staff level, and identify tools valid for group process measurement. THEORY AND METHODS: A systematic literature review utilizing three interrelated searches was conducted. Thematic analysis techniques were supported by NVivo 7 software. Complexity theory was used to guide the analysis, ground the new conceptualization and validate the selected measures. Other properties of the measures were critiqued using established criteria. RESULTS: There is a need for a convergent view of what constitutes a partnership and its measurement. The salient attributes of IHSSP and their interorganizational context were described and grounded within complexity theory. Two measures were selected and validated for measurement of proximal group outcomes. CONCLUSION: This paper depicts a novel complexity theory-based conceptual model for IHSSP of front-line staff who provide chronic illness care. The conceptualization provides the underpinnings for a comprehensive evaluative framework for partnerships. Two partnership process measurement tools, the PSAT and TCI are valid for IHSSP process measurement with consideration of their strengths and limitations.
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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 | 0.032 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.002 |
| 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".