Translation and validation of Training Needs Analysis Questionnaire among reproductive, maternal and newborn health workers in Tanzania
Bibliographic record
Abstract
BACKGROUND: Continuous professional development (CPD) has been reported to enhance healthcare workers' knowledge and skills, improve retention and recruitment, improve the quality of patient care, and reduce patient mortality. Therefore, validated training needs assessment tools are important to facilitate the design of effective CPD programs. METHODS: A cross-sectional survey was conducted using self-administered questionnaires. Participants were healthcare workers in reproductive, maternal, and neonatal health (RMNH) from seven hospitals, 12 health centers, and 17 dispensaries in eight districts of Mwanza Region, Tanzania. The training needs analysis (TNA) tool that was used for data collection was adapted and translated into Kiswahili from English version of the Hennessy-Hicks' Training Need Analysis Questionnaire (TNAQ). RESULTS: In total, 153 healthcare workers participated in this study. Most participants were female 83 % (n = 127), and 76 % (n = 115) were nurses. The average age was 39 years, and the mean duration working in RMNH was 7.9 years. The reliability of the adapted TNAQ was 0.954. Assessment of construct validity indicated that the comparative fit index was equal to 1. CONCLUSIONS: The adapted TNAQ appears to be reliable and valid for identifying professional training needs among healthcare workers in RMNH settings in Mwanza Region, Tanzania. Further studies with larger sample sizes are needed to test the use of the TNAQ in broader healthcare systems and settings.
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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".