Measuring health workers’ motivation composition: validation of a scale based on Self-Determination Theory in Burkina Faso
Bibliographic record
Abstract
BACKGROUND: Although motivation of health workers in low- and middle-income countries (LMICs) has become a topic of increasing interest by policy makers and researchers in recent years, many aspects are not well understood to date. This is partly due to a lack of appropriate measurement instruments. This article presents evidence on the construct validity of a psychometric scale developed to measure motivation composition, i.e., the extent to which motivation of different origin within and outside of a person contributes to their overall work motivation. It is theoretically grounded in Self-Determination Theory (SDT). METHODS: We conducted a cross-sectional survey of 1142 nurses in 522 government health facilities in 24 districts of Burkina Faso. We assessed the scale's validity in a confirmatory factor analysis framework, investigating whether the scale measures what it was intended to measure (content, structural, and convergent/discriminant validity) and whether it does so equally well across health worker subgroups (measurement invariance). RESULTS: Our results show that the scale measures a slightly modified version of the SDT continuum of motivation well. Measurements were overall comparable between subgroups, but results indicate that caution is warranted if a comparison of motivation scores between groups is the focus of analysis. CONCLUSIONS: The scale is a valuable addition to the repository of measurement tools for health worker motivation in LMICs. We expect it to prove useful in the quest for a more comprehensive understanding of motivation as well as of the effects and potential side effects of interventions intended to enhance motivation.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".