Moral distress among health care workers and decision-makers undermines tuberculosis infection screening and treatment programs for migrants in Canada: a reflexive thematic analysis
Bibliographic record
Abstract
BACKGROUND: Routine screening of new immigrants for tuberculosis (TB) infection is being adopted or considered in high-income countries with a low burden of TB disease, such as Canada, but has major ethical implications due to the excess burden of costs and risks placed on migrants. We conducted a pragmatic qualitative study to understand health care providers’ and decision-makers’ perspectives on health care access, policy and ethics associated with screening and treating TB infection among new immigrants. METHODS: Between Aug. 2023 and Feb. 2024, we conducted semi-structured interviews with 20 health care providers and public health decision-makers in five Canadian cities. We constructed themes inductively via reflexive thematic analysis of transcripts, guided by pre-existing ethical concepts in public health policy related to risk imposition and the just distribution of risk. FINDINGS: In the current political and health care landscape, TB providers and decision-makers who work with new immigrants and TB programs expressed experiences of moral distress due to an inability to fulfil their clinical and moral duty towards new immigrants. They attributed this to disconnected health and immigration policies, as well as high degrees of uncertainty around individual and societal benefits of an immigration-based TB infection screening program. The moral distress manifests as general reluctance among health providers to pursue TB infection screening and TPT for new immigrants. CONCLUSION: The moral distress experienced by TB providers and decision-makers undermines the potential health impact of immigration-based screening. Our study highlights crucial faults within current health and immigration policies that fuel their distress, including the lack of: a ‘firewall’ between TB infection screening and the immigration process; facilitation of trust-building and informed decision-making; and reciprocity for the disproportionate burden of costs and risks placed on immigrants.
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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 | 0.029 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.035 | 0.022 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.003 | 0.005 |
| 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".