Living in a Material World: Reflecting on Some Assumptions of Health Psychology
Bibliographic record
Abstract
AS WE ENTER the 21st century a major challenge for health psychologists is to reflect on the adequacy of our theories and methods for improving the health of the world's masses.While many of us may think that our theories developed in the quiet of the academic seminar room are at least benign, the evidence suggests that this may not be the case.For example, in a recent review Waldo and Coates (2000) considered the role of behavioural science, and implicitly of health psychology, in the worldwide programme to develop a strategy to halt the spread of AIDS, the most relentless infectious disease that has led to the deaths of millions in the developing world.They argued that the very theoretical assumptions of health psychology have actually hindered attempts to control this epidemic.Through persistently directing attention towards the individual level of analysis in explaining health-related behaviours, health psychology has contributed to masking the role of economic, political and symbolic social inequalities in patterns of ill-health, both globally and within particular C ATHERINE C AMPBELL is Reader in Social Psychology at the London School of Economics and Political Science.Her research interests are in the areas of HIV/AIDS, particularly in less affluent countries, social capital, community participation and public health.
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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 | 0.033 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.123 |
| Scholarly communication | 0.016 | 0.031 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.011 | 0.031 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".