Predict, prevent and manage moral injuries in Canadian frontline healthcare workers and leaders facing the COVID-19 pandemic: Protocol of a mixed methods study
Bibliographic record
Abstract
Moral injuries can occur when perpetrating, failing to prevent, or bearing witness to acts that transgress deeply held moral beliefs and expectations. The COVID-19 crisis highlighted the fact that psychosocial stressors at work, such as high emotional demands, are placing Canadian healthcare workers at risk of moral injuries. Evidence linking psychosocial stressors at work to moral injuries are needed to better predict, prevent and manage moral injuries, as these stressors are frequent and modifiable occupational risk factors. This protocol presents a study aiming to: 1) understand workplace events having the potential to either cause or reduce moral injuries, 2) predict the risk and severity of moral injuries using a disease prevention model, 3) identify biological signatures (biomarkers) associated with psychosocial stressors at work and moral injuries and 4) elaborate preliminary guidelines of organizational practices for frontline healthcare workers to reduce and manage moral injuries. This study is a mixed methods research with three components: qualitative, quantitative and biological. The data collection has been completed and because of the COVID-19 pandemic, it was adjusted to allow for gathering qualitative and quantitative data remotely. Frontline healthcare workers and leaders were included. Through focus groups and individual interviews, and an online questionnaire, events and psychosocial working conditions that may increase the risk of moral injuries will be documented. In addition, blood samples which were collected from a sub-sample of volunteer participants will measure an innovative set of biomarkers associated with vulnerability to stress and mental health. Data analyses are ongoing. We anticipate to identify workplace events that may trigger moral injuries. We expect that potential predictors of moral injury risk occurrence and severity will be identified from psychosocial stressors at work that can be improved by implementing organizational practices. We also expect to observe a different mental health state and biological inflammation signature across workers exposed compared to workers not exposed to psychosocial stressors at work. Based on these future findings, we intend to develop preliminary recommendations of organizational practices for managers. This research will contribute to expand our knowledge of the events in the workplace likely to generate or lessen the impact moral injuries, to build a model for predicting the risk of moral injuries at work, all in the specific context of the COVID-19 health crisis among healthcare workers.
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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 | 0.009 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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".