Healthcare Workers and Decision-Makers’ Understanding of Liberty, Harm, and the Harm Principle in the Case of Tuberculosis in Persons with Severe and Persistent Mental Illnesses
Bibliographic record
Abstract
Background: The harm principle, which generally states that individuals are free to act so long as they do not harm other non-consenting persons, is commonly invoked to help determine the boundaries between an individual’s liberty and the public good in public health and mental health. The case of tuberculosis (TB) in persons with severe and persistent mental illnesses (SPMI) provides the opportunity to evaluate the application of the harm principle in a real-world context. TB in persons with SPMI is an ideal case study because (a) the treatment of TB and SPMI are two examples where liberty restrictions are often justified by appealing to the harm principle and (b) the goal of arresting the spread of TB (e.g. through isolation) may conflict with the goals of mental health (e.g. reducing social isolation). However, it is unknown how healthcare workers in public health and mental health understand the notion of liberty and harm, and moreover, whether their views resonate with philosophical literature. Methods: A mixed methods study that includes interviews and an online survey with healthcare workers and decision-makers in public health units and mental health centres in Toronto, Canada. Twenty interviews were conducted and analyzed via thematic analysis. An online survey, where items were generated by reference to philosophical interpretations of the harm principle (including JS Mill, Joel Feinberg, Joseph Raz, and Immaunel Kant), was distributed and analyzed via factor analysis and t-tests. The response rate was 41.5% (n=91). Results: Six themes emerged from the interviews, including accounting for the context surrounding liberty restrictions and the importance of supporting persons who have their liberty restricted for the greater public good. The survey findings suggest statistically significant difference between TB workers and mental health workers regarding the importance of directly observed therapy (DOT) and conceptions of risk. Conclusion: The findings provide an understanding of how notions of liberty and harm are understood by public health and mental health workers. The participants demonstrated nuanced moral reasoning that can enrich and provide real-world context to the existing ethics literature on the topic of the harm principle and liberty restrictions.
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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 | 0.050 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.033 | 0.073 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 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".