‘Health psychology’ or ‘psychology for health’? A history of psychologists’ engagement with health in South Africa
Bibliographic record
Abstract
In contrast to the institutionalization of health psychology in North America and Europe, much psychological work on health issues in South Africa emerged as part of a critical revitalization of South African psychology as a whole, coinciding with the dismantling of Apartheid and global shifts in health discourse. The field's development reflects attempts to engage with urgent health problems in the context of rapid sociopolitical changes that followed democratic transition in the 1990s, and under new conditions of knowledge production. We provide an account of these issues, as well as reflections on the field's future, as inflected through the experiences of 12 South African psychologists whose careers span the emergence of health-related psychology to the present day.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.027 | 0.051 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.004 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 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".