Managerial Capacity of Health Care Managers in Primary Health Care Centres in Ekiti State, Nigeria
Bibliographic record
Abstract
Background: The performances of Primary Health Care workers play key roles in delivering quality health care thereby leading to health systems’ strengthening. This study assessed the managerial capacity of health care managers at Primary Health Care centres in Ekiti State, Nigeria.Methods: A descriptive cross-sectional study was carried out amongst 188 health care managers in all the Local Government Areas of Ekiti State, Nigeria from August to November 2020. Self-administered questionnaires (google form) were used to obtain data. Analysis of data was done with STATA SE 12. The level of significance (p-value) was set at <0.05.Results: The mean age of respondents was 45.9 ± 7.0 years with females 161 (85.6%) and males 27 (14.4%). More than one-third 69 (36.7%) of respondents had received formal training in managerial and leadership development. The majority of respondents (94.7%) had good managerial capacity with a mean total score of 279.8 ± 46.9. There was a statistically significant difference in the average managerial capacity scores across the level of education and professional experience categories. Respondents who had tertiary education and those with more than 5 years’ experience had higher managerial capacity scores compared to others. (p < 0.001)Conclusion: The findings indicated that majority of the Primary Health Care managers in Ekiti State had a good managerial capacity to run the Primary Health Care systems. It is recommended that individual health workers should continue with personal development and continuous institutional development by the state government is encouraged towards improvement of health services rendered. Keywords: Managerial capacity; Healthcare managers; Primary Health Care; Institutional development
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".