A strategic analysis of health behaviour change initiatives in Africa
Bibliographic record
Abstract
BACKGROUND: Changed health behaviours can contribute significantly to improved health. Consequently, significant investments have been channelled towards health behaviour change initiatives in Africa. Health behaviour change initiatives that address social, economic and environmental levers for behaviour change can create more sustained impact. OBJECTIVES: Through a scoping study of the literature, we explored the literature on behaviour change initiatives in Africa, to assess their typologies. We explored whether the availability of initiatives reflected country demographic characteristics, namely life expectancy, gross domestic product (GDP), and population sizes. Finally, we assessed topical themes of interventions relative to frequent causes of mortality. METHODS: We used the Behaviour Change Wheel intervention categories to categorise each paper into a typology of initiatives. Using Pearson's correlation coefficient, we explored whether there was a correlation between the number of initiatives implemented in a country in the specified period, and socio-demographic indicators, namely, GDP per capita, total GDP, population size, and life expectancy. RESULTS: Almost 64% of African countries were represented in the identified initiatives. One in five initiatives was implemented in South Africa, while there was a dearth of literature from Central Africa and western parts of North Africa. There was a positive correlation between the number of initiatives and GDP per capita. Most initiatives focused on addressing sexually transmitted infections and were short-term trials and/or pilots. Most initiatives were downstream focused e.g. with education and training components, while upstream intervention types such as the use of incentives were under-explored. CONCLUSION: We call for more emphasis on initiatives that address contextual facilitators and barriers, integrate considerations for sustainable development, and consider intra-regional deprivation.
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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 | 0.011 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.013 | 0.019 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".