Impact of financial incentives introduced during the COVID-19 pandemic on nursing staff: a mixed-method protocol
Bibliographic record
Abstract
INTRODUCTION: The COVID-19 pandemic has had a major impact on nursing staff, resulting in alarming turnover rates. As part of the Quebec (Canada) government's response to the pandemic, nurses have been offered exceptional financial incentives. Considering the cost of these measures, the current article presents the research protocol of a study aiming to explore the impact of financial incentives on full-time equivalent, and retention rates among the nursing staff in two healthcare settings in Quebec. METHODS AND ANALYSIS: A sequential mixed design (QUANT→QUAL) will be used. The quantitative phase will involve a quantitative descriptive analysis and the qualitative phase will consist of a qualitative descriptive study. Administrative data (working hours, employment status and retention rate) will be analysed over a 4.5-year follow-up (from 1 January 2019 to 30 June 2023) to explore the impact of the financial incentives. Focus groups will explore nurses' views on financial incentives. The results will inform the development of future interventions to mitigate attrition problems among nurses and ultimately improve access to and the continuity of public health services. ETHICS AND DISSEMINATION: The study has been approved by ethics committees of the participating healthcare settings (Comité d'éthique de la recherche sectorial en santé des populations et première ligne du CIUSSS de la Capitale-Nationale; Comité d'éthique de la recherche du CISSS de Chaudière-Appalaches). The results will be disseminated mainly in scientific publications and at academic conferences in addition to presentations tailored to various non-academic audiences.
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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 | 0.123 | 0.078 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.053 | 0.010 |
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".