Cross‐sector alliances for large‐scale health leadership development in Canada
Bibliographic record
Abstract
Purpose This paper aims to examine the benefits and challenges of enacting cross‐sector alliances as a strategy to meet the health leadership capacity and capability requirements to effect improvements in health service delivery. Design/methodology/approach The findings originate from two case studies of cross‐sector alliances in Canada. Findings Value generated by strategic alliances in health with organisations from public, private and civil sectors is accrued at the inter‐organisational, organisational, group and individual level. Obstacles related to mindsets, operations and governance guiding the partnerships were identified which further an understanding of the advantages and constraints for using cross‐sector alliances as a strategy for large‐scale health leadership development. Research limitations/implications Future research could investigate whether other factors influence the overall success of using an alliance strategy which may lead to a more comprehensive understanding of large‐scale health leadership initiatives. Given the universal health care context of this study, the results should be examined for their generalisability to other contexts. Practical implications The results urge decision‐makers to develop the mental models, behaviours and processes that support the use of cross‐sector alliances to achieve practical benefits gained through large‐systems health leadership development that may otherwise be unattainable. Originality/value This paper responds to the needs of executives by investigating alliances among health, education, business and government as a strategic driver for building the health leadership capacity and capability needed for implementing health reform.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".