Evaluation of a leadership development impact assessment toolkit: a comparative case study of experts’ perspectives in three Canadian provinces
Bibliographic record
Abstract
PURPOSE: This paper aims to explore users' perceptions of whether the Leadership Development Impact Assessment (LDI) Toolkit is valid, reliable, simple to use and cost-effective as a guide to its quality improvement. DESIGN/METHODOLOGY/APPROACH: The Canadian Health Leadership Network codesigned and codeveloped the LDI Toolkit as a theory-driven and evidence-informed resource that aims to assist health-care organizational development practitioners to evaluate various programs at five levels of impact: reaction, learning, application, impact and return on investment (ROI) and intangible benefits. A comparative evaluative case study was conducted using online questionnaires and semistructured telephone interviews with three health organizations where robust leadership development programs were in place. A total of seven leadership consultants and specialists participated from three Canadian provinces. Data were analyzed sequentially in two stages involving descriptive statistical analysis augmented with a qualitative content analysis of key themes. FINDINGS: Users perceived the toolkit as cost-effective in terms of direct costs, indirect costs and intangibles; they found it easy-to-use in terms of clarity, logic and structure, ease of navigation with a coherent layout; and they assessed the sources of the evidence-informed tools and guides as appropriate. Users rated the toolkit highly on their perceptions of its validity and reliability. The analysis also informed the refinement of the toolkit. ORIGINALITY/VALUE: The refined LDI Toolkit is a comprehensive online collection of various tools to support health organizations to evaluate the leadership development investments effectively and efficiently at five impact levels including ROI.
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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 | 0.032 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.024 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".