Feasibility and ethical issues: experiences and concerns of healthcare workers regarding a new RSV prophylaxis programme in Nunavik, Quebec
Bibliographic record
Abstract
Background: The respiratory syncytial virus (RSV) is a major cause of hospitalisation in young Inuit children. Prophylaxis with palivizumab is routinely recommended for premature infants and those with severe pulmonary or cardiac diseases. In the fall 2016, the Quebec Ministry of Health expanded the criteria to include healthy full-term (HFT) newborns from Nunavik based on their high RSV hospitalisation rates.Objectives: The aim of this study was to describe the impact of this programme on Nunavik health services during the first RSV season after its implementation (2016–2017) by studying challenges, concerns and needs of healthcare workers (HCWs).Methods: An ethnographic approach was used. Semi-structured interviews focusing on HCWs experiences, and opinions to improve the new programme were conducted with 20 HCWs involved in its implementation.Results: Main reported challenges and concerns were: additional work(over)load, lack of information and evidence about the need and efficacy of palivizumab in HFT newborns, communication issues between stakeholders, and ethical issues regarding the Inuit population.Conclusion: The study revealed significant feasibility and acceptability issues. The programme was highly resource consuming. To address HCWs’ concerns, evidence-based data regarding palivizumab effectiveness in HFT infants, as well as consultation and involvement of Inuit population are warranted.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.020 | 0.007 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".