Significance of informal (on-the-job) learning and leadership development in health systems: lessons from a district finance team in South Africa
Bibliographic record
Abstract
BACKGROUND: The district health system (DHS) has a critical role to play in the delivery of primary healthcare (PHC). Effective district management, particularly leadership is considered to be crucial element of the DHS. Internationally, the debate around developing leadership competencies such as motivation or empowerment of staff, managing relationships, being solution driven as well as fostering teamwork are argued to be possible through approaches such as formal and informal training. Despite growing multidisciplinary evidence in fields such as engineering, computer sciences and health sciences there remains little empirical evidence of these approaches, especially the informal approach. Findings are based on a broader doctoral thesis which explored district financial management; although the core focus of this paper draws attention to the significance of informal learning and its practical value in developing leadership competencies. METHODS: A qualitative case study was conducted in one district in the Gauteng province, South Africa. Purposive and snowballing techniques yielded a sample of 18 participants, primarily based at a district level. Primary data collected through in-depth interviews and observations (participant and non-participant) were analysed using thematic analysis. FINDINGS: Results indicate the sorts of complexities, particularly financial management challenges which staff face and draws attention to the use of two informal learning strategies-learning from others (how to communicate, delegate) and fostering team-based learning. Such strategies played a role in developing a cadre of leaders at a district level who displayed essential competencies such as motivating staff, and problem solving. CONCLUSIONS: It is crucial for health systems, especially those in financially constrained settings to find cost-effective ways to develop leadership competencies such as being solution driven or motivating and empowering staff. This study illustrates that it is possible to develop such competencies through creating and nurturing a learning environment (on-the-job training) which could be incorporated into everyday practice.
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 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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".