Psychosocial Risks among Quebec Healthcare Workers during the COVID-19 Pandemic: A Social Media Analysis
Bibliographic record
Abstract
During the COVID-19 pandemic, healthcare workers (HCWs) were at high risk of exposure to the SARS-CoV-2 virus and to work-related psychosocial risks, such as high psychological demands, low social support at work and low recognition. Because these factors are known to be detrimental to health, their detection and mitigation was essential to protect the healthcare workforce during the pandemic, when this study was initiated. Therefore, using Facebook monitoring, this study aims to identify the psychosocial risk factors to which HCWs in Quebec, Canada reported being exposed at work during the first and second pandemic waves. In this study, HCWs mainly refer to nurses, respiratory therapists, beneficiary attendants and technicians (doctors, managers and heads of healthcare establishments were deemed to be less likely to have expressed work-related concerns on the social media platforms explored). A qualitative exploratory research based on passive analysis of Facebook pages from three different unions was conducted. For each Facebook page, automatic data extraction was followed by and completed through manual extraction. Posts and comments were submitted to undergo thematic content analysis allowing main coded themes to emerge based on known theoretical frameworks of the psychosocial work environment. In total, 3796 Facebook posts and comments were analyzed. HCWs reported a variety of psychosocial work exposures, the most recurrent of which were high workload (including high emotional demands), lack of recognition and perceived injustice, followed by low workplace social support and work-life conflicts. Social media monitoring was a useful approach for documenting the psychosocial work environment during the COVID-19 crisis and could be a useful means of identifying potential targets for preventive interventions in future sanitary crises or in a context of major reforms or restructuring.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".