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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.022 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".