Ethics, effectiveness and population health information interventions: a Canadian analysis
Bibliographic record
Abstract
Population health information interventions (PHIIs) use information in efforts to promote health. PHIIs may push information to a target audience (communication), pull information from the public (surveillance), or combine both in a bidirectional intervention. Although PHIIs have often been framed as non-invasive and ethically innocuous, in reality they may be intrusive into people's lives, affecting not only their health but their senses of security, respect, and self-determination. Ethical acceptability of PHIIs may have impacts on intervention effectiveness, potentially giving rise to unintended consequences. This article examines push, pull, and bidirectional PHIIs using empirical data from an ethnographic study of young mothers in Greater Vancouver, Canada. Data were collected from October 2013 to December 2014 via naturalistic observation and individual interviews with 37 young mothers ages 16-22. Transcribed interviews and field notes were analyzed using inductive qualitative thematic analysis. Both push and pull interventions were experienced as non-neutral by the target population, and implementation factors on a structural and individual scale affected intervention ethics and effectiveness. Based on our findings, we suggest that careful ethical consideration be applied to use of PHIIs as health promotion tools. Advancing the 'ethics of PHIIs' will benefit from empirical data that is informed by information and computer science theory and methods. Information technologies, digital health promotion services, and integrated surveillance programs reflect important areas for investigation in terms of their effects and ethics. Health promotion researchers, practitioners, and ethicists should explore these across contexts and populations.
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How this classification was reachedexpand
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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".