Development and validation of a tool to assess core competencies of public health professionals in low-income settings: findings from Uttar Pradesh, India
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Bibliographic record
Abstract
BACKGROUND: Many low- and middle-income countries (LMICs) lack instruments to measure gaps in the public health competency of health professionals. The objective of this study is to develop a validated and reliable Core Public Health Competency (COPHEC) index by assessing the knowledge, skills, abilities, and attitudes of senior and mid-level public health professionals with supervisory and management responsibilities in Uttar Pradesh (UP), India. METHODS: Using the Core Competency framework that was developed in UP, we generated a draft COPHEC tool with 37 items, measured on a four-point Likert scale. We administered the tool to a total of 166 public health professionals that included two samples-84 senior and 82 mid-level public health professionals. To extract factors and assign factor scores to the instrument, we performed an exploratory factor analysis (EFA) using principal component analysis (PCA). Content and face validities were assessed by examining the steps used for the construction of the draft tool. Construct validity was measured by assessing the average factor loading of the items onto the component extracted from EFA. Internal consistency was used as a measure of reliability. RESULTS: The final COPHEC index had 37 items loaded on one factor in the sample. Content and face validities were assured through a participatory process with relevant stakeholders who identified the initial set of items as part of a Core Competency framework. Construct validity of the COPHEC scale was confirmed by the high average factor loading of components ranging from 0.58 to 0.81. The final index showed adequate reliability with Cronbach's alpha (α) = 0.97. CONCLUSIONS: The COPHEC index is a valid and reliable measure of core competencies in public health in UP. We recommend that governments adapt the index in LMICs to conduct assessments of health workers to identify training needs, evaluate the effectiveness of training programs through participants' competency acquisition pre- and post-training, and inform workforce development efforts in recruitment and performance management.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it