Evaluating Leadership Development Needs in a Health Care Setting Through a Partnership Approach
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
The Problem Strategic HRD contributes to creating an environment in which objectives and improved performance can be realized through leadership development (LD). Despite considerable investment in LD by public and private sector organizations in North America and Europe, these efforts often fail to produce significant changes in leaders’ behaviors, organizational culture, or organizational performance. As a result, too often leadership development programs (LDPs) are “one size fits all” or a prepackaged competency model without paying attention to individual and contextual differences. A more collaborative approach in the design and delivery of LD has been advocated; however, to date little research has documented or evaluated this at the pre-LDP or needs assessment stage. The Solution This research argues that LD should be a collaborative process involving all stakeholders and that such a partnership approach starts at the needs assessment phase. The research documents and evaluates a three-phase LD needs assessment process in a health care setting. Adopting a case study methodology, it draws primarily upon qualitative data collected from focus groups, written submissions, and interviews with senior and middle managers employed in a provincial health authority, Horizon Health Network, located in Atlantic Canada. The Stakeholders HRD researchers and practitioners in health care responsible for designing, delivering, and evaluating LDPs will find the approach described here insightful and practical. Middle and senior managers working in health care settings who seek to find practical and effective means of addressing leadership gaps and building and sustaining leadership competence across organizations under the pressures of persistent and complex change will also find this research relevant and valuable.
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.
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it