Succession Planning and Leadership Development in a Faculty of Health Sciences
Bibliographic record
Abstract
BACKGROUND: The challenges in the Faculty of Health Sciences in universities are unique and complex. These dynamic faculties require decisive and strong leadership, smooth systems and business processes, succession plans and development opportunities. These challenges in these faculties are exacerbated by the fact that the roles and responsibilities of the Heads of Departments are multifaceted. Those who are heading them are not acclimatising to the responsibilities because of the lack of support to develop and succeed in their positions. This study therefor seeks to determine challenges faced by the HoDs in the Faculty of Health Sciences, and the succession plans, competences and development programmes that are in place to develop future leadership talent in a university in South Africa. METHODS: A single case study approach was employed whereby qualitative in-depth interviews were conducted with nine HoDs and a faculty dean as well as the observation and document analysis. The grounded theory as an inductive process was used to categorise and code data where themes and trends emerged from the data. RESULTS: Four major themes emerged from the data: The university, faculty and departments within the faculty did not have succession plans while the strategic positions were occupied by post-retirees. The Faculty did not have competent employees who could ascend to academic leadership positions in the faculty. There was limited growth in the Faculty due to the absence of the developmental programmes. There was a plethora of challenges including the shortage of office equipment, absence of the mentoring programmes, structural fragmentation and inefficiencies in the Human Resource Department (HRD). CONCLUSIONS: A pervasive crisis was observed in this particular Faculty of Health Sciences during the turbulent times in universities. It is crucial for the HoDs to receive quality support from the administrative departments such as HRD in order for them to achieve the departmental objectives. The HoDs’ leadership capabilities and effectiveness can be enhanced by mainstreaming the human resources business systems, development, mentoring and coaching, as well as the implementation of well-coordinated succession plans.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".