Leadership development needs assessment in healthcare: a collaborative approach
Bibliographic record
Abstract
Purpose This exploratory study aims to present data collected from a collaborative project designed to assess leadership development needs in a healthcare setting. Design/methodology/approach The research describes a three‐phase design that draws primarily upon qualitative data collected from focus groups, written submissions and interviews with middle managers employed in a provincial health authority, Horizon Health Network, located in Atlantic Canada. Findings The findings reveal a number of considerations for future leadership development programs including the need to make space for leadership development, the role of partnerships in leadership development, and the need for mentoring and coaching. In addition, a number of challenges facing the organization and the possible impact on leadership development are identified. Research limitations/implications The findings are based upon one case study site and this limits the generalizability of the research. In addition, the researchers were only able to make direct contact with one half of the 150 middle managers that will be participants in the eventual leadership development program. Practical implications This research describes a collaborative approach through which to increase buy‐in and commitment to leadership development in healthcare organizations. The approach provides a path to build sustainability in overall organizational performance through a healthy and engaged workforce. Originality/value Most research describes or evaluates leadership development programs with little attention devoted to the process of needs assessment. In addition, the literature focuses upon participants who have finished a program or are part way through a program. This research looks at the possibilities of a collaborative approach to leadership development pre‐leadership development program starting at the needs assessment phase.
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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 | 0.076 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".