A Partnership Model Evolves from a Living Inventory of Engagement
Bibliographic record
Abstract
This university-community partnership developed when practitioners at a community health centre within a regional health authority collaborated with nursing faculty at the University of New Brunswick in Saint John, New Brunswick, Canada. A philosophical match and work context facilitated this collaboration. Partners acknowledged that continued maintenance of the partnership, or its termination, was dependent on the acceptance of principles of partnership engagement. Even though the common philosophy of primary healthcare brought these partners together and reciprocal capacity building was the glue that nurtured their partnership, the tensions that arose fuelled its continued growth and development. Roles and responsibilities of members were discerned and space was created to acknowledge and integrate the lessons learned when tensions were examined. Within this environmental context, a model of partnership emerged. Tensions within this model were accepted as opportunities that resulted in beliefs being challenged, relevant questions generated and partnership alliances strengthened. In this spiral process, new relevant knowledge was constructed and new norms for best practice and policy were developed. The utility of this inter-professional Partnership Model: Living Inventory of Engagement is demonstrated through case examples.
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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 | 0.014 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".