Long-Term Residential Care Worker Mental Health: The Power of Public Recognition During the COVID-19 Pandemic
Bibliographic record
Abstract
Context: Facing unprecedented barriers to providing adequate care, along with a lack of recognition from the public, long-term residential care (LTRC) workers were at risk for mental health concerns, particularly moral distress, during the COVID-19 pandemic. Objective and Methods: This analysis of 30 interviews with LTRC workers aimed to describe how workers were affected by the public during the COVID-19 pandemic. Guided by recognition theory, our thematic analysis identified patterned meanings of worker experiences with the interface between LTRC facilities and the public. Findings: LTRC workers’ interactions with the public often reflected a lack of recognition for workers, as workers, and their workplaces, were publicly criticised while attempting to manage new and difficult responsibilities to members of the public. Yet, instances of recognition from the public had the potential to support workers’ self-confidence, self-respect and self-esteem. LTRC workers’ experiences pointed to a need for better understanding from members of the public as part of alleviating their stress. Limitations: The interviews were not originally conducted to examine the specific research question of this analysis, and we do not imply a diagnosis of participants’ mental health. The findings may be limited by self-selection bias. Implications: This study highlights the importance of having workers’ stories shared as part of increasing public awareness of their experiences and reducing the public’s negative perceptions of their work.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 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".