Working towards universal health coverage: a qualitative study to identify strategies for improving student enrolment for the pre-service training of nurses, midwives and community health workers in Nigerian health training institutions
Bibliographic record
Abstract
BACKGROUND: Student enrolment processes and practices can affect the quality of pre-service training programmes. These processes and practices may have serious implications for the quality and quantity of students within health training institutions, the quality of education for prospective health workers and consequently health workforce performance. This study assessed current student enrolment processes and practices for nurses, midwives and community health workers within health training institutions in two Nigerian states, so as to identify strategies for improving student enrolment for these key cadres of frontline health workers. METHODS: This study was carried out in Bauchi and Cross-River States, which are the two Human Resources for Health (HRH) project focal states in Nigeria. Utilizing a qualitative research design, 55 in-depth interviews and 13 focus group discussions were conducted with key stakeholders including students and tutors from pre-service health training institutions as well as policy-makers and public sector decision-makers from Ministries of Health, Government Agencies and Regulatory Bodies. Study participants were purposively sampled and the qualitative data were audio-recorded, transcribed and then thematically analysed. RESULTS: Study participants broadly described the application process to include the purchase, completion and submission of application forms by prospective students prior to participation in entrance examinations and oral interviews. The use of 'weeding examinations' during the student enrolment process, especially in Bauchi state, was identified as a useful quality assurance mechanism for the pre-service training programmes of frontline health workers. Other strategies identified by stakeholders to address challenges with student enrolment include sustained advocacy to counter-cultural norms and gender stereotypes vis-à-vis certain professions, provision of scholarships for trainee frontline health workers and ultimately the development as well as effective implementation of national and state-specific policy and implementation guidelines for the student enrolment of key frontline health workers. CONCLUSION: While there are challenges which currently affect student enrolment for nurses, midwives and community health workers in Nigeria, this study has proposed key strategies which if carefully considered and implemented can substantially improve the status quo. These will probably have far-reaching implications for improving health workforce performance, population health outcomes and efforts to achieve universal health coverage.
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How this classification was reachedexpand
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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".