Learning and development dimensions of a pan‐Canadian primary health care capacity‐building project
Bibliographic record
Abstract
Purpose The purpose of this paper is to use a descriptive case study to establish how collaboration, innovation and knowledge‐management strategies have scaled‐up learning and development in rural, remote and other resource‐constrained Canadian delivery settings. Design/methodology/approach Intervention design was realized through a one‐time, collaborative, national capacity‐building project. A project portfolio of 72 sub‐projects, initiatives and strategic activities was used to improve access, enhance quality and create capacity for palliative and end‐of‐life care services. Evaluation was multifaceted, including participatory action research, variance analysis and impact analysis. This has been supplemented by post‐intervention critical reflection and integration of relevant literature. Findings The purposeful use of collaboration, innovation and knowledge‐management strategies have been successfully used to support a rapid scaling‐up of learning and development interventions. This has enabled enhanced and new pan‐Canadian health delivery capacity implemented at the local service delivery catchment‐level. Research limitations/implications The intervention is bounded by a Canada‐specific socio‐cultural/political context. Design variables and antecedent conditions may not be present and/or readily replicated in other nation‐state contexts. The findings suggest opportunities for future integrative and applied health services and policy research, including collaborative inquiry that weaves together concepts from adult learning, social science and industrial engineering. Practical implications Scaling‐up for new capacity is ideally approached as a holistic, multi‐faceted process which considers the total assets within delivery systems, service catchments and communities as potentially being engaged and deployed. Originality/value The Pallium Integrated Capacity‐building Initiative offers model elements useful to others seeking theory‐informed practices to rapidly and effectively scale‐up learning and development efforts.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.023 | 0.011 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".