Return on investment in healthcare leadership development programs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose Strong leadership has been shown to foster change, including loyalty, improved performance and decreased error rates, but there is a dearth of evidence on effectiveness of leadership development programs. To ensure a return on the huge investments made, evidence-based approaches are needed to assess the impact of leadership on health-care establishments. As a part of a pan-Canadian initiative to design an effective evaluative instrument, the purpose of this paper was to identify and summarize evidence on health-care outcomes/return on investment (ROI) indicators and metrics associated with leadership quality, leadership development programs and existing evaluative instruments. Design/methodology/approach The authors performed a scoping review using the Arksey and O'Malley framework, searching eight databases from 2006 through June 2016. Findings Of 11,868 citations screened, the authors included 223 studies reporting on health-care outcomes/ROI indicators and metrics associated with leadership quality (73 studies), leadership development programs (138 studies) and existing evaluative instruments (12 studies). The extracted ROI indicators and metrics have been summarized in detail. Originality/value This review provides a snapshot in time of the current evidence on ROI indicators and metrics associated with leadership. Summarized ROI indicators and metrics can be used to design an effective evaluative instrument to assess the impact of leadership on health-care organizations.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it