Strategies to Enhance Leadership Development of Midlevel Managers
Bibliographic record
Abstract
Organizational executives annually invest more than $50 billion in leadership development worldwide. Nonetheless, human resources (HR) managers are concerned that leadership development initiatives prove inadequate in delivering learning outcomes equal to the investment, leaving midlevel managers ill-prepared to lead. Guided by experiential learning theory, this qualitative multiple-case study was conducted to explore strategies HR managers use to enhance the leadership development of midlevel managers. A purposeful sample included three HR managers from three organizations located in a west coast metropolitan area in Canada who successfully implemented leadership development strategies. Data were collected from semistructured interviews and organizational documents. Informed by Yin’s five-step case-study approach, four themes emerged: (a) employ multichannel learning, (b) cultivate a leadership mindset, (c) conduct coaching support, and (d) collaborate for enhanced leadership development outcomes. A key recommendation is for HR managers to create a supportive organizational culture by ensuring sufficient resources are allocated for leadership development initiatives. The implications for positive social change include the potential for skilled leaders to help community agencies flourish by expanding cooperative social bonds, enhancing trust and respect, and strengthening shared values and social responsibility.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".