Staff mental health while providing care to people with intellectual disability during the COVID‐19 pandemic
Bibliographic record
Abstract
Background: The COVID-19 pandemic has placed enormous strain on health systems around the world, undermining the mental health and wellbeing of healthcare workers. Supporting people with intellectual disabilities may be particularly challenging for workers, as some people with intellectual disabilities may have a limited understanding of the pandemic, and find it challenging to adhere to the restrictions imposed by public health guidelines such as social distancing, lockdowns and change in usual routine and activities. In addition, many people with intellectual disabilities have increased vulnerability to more negative effects of COVID-19, with significantly higher mortality rates. Although there is emerging research on the mental health of healthcare staff during this time, there has been little specific work on the mental health of staff working with people with intellectual disability, particularly a lack of qualitative research. Methods: The current study employed semi-structured interviews with 13 healthcare workers (12 women and 1 man) who were working with people with intellectual disability during the COVID-19 pandemic. The interview data were analysed using thematic content analysis. Findings: The participants spoke in depth about the challenges of the working environment, the impact of providing care during the pandemic on staff mental health, supporting staff mental health and wellbeing and learning for the future. Conclusions: Systematic efforts are required to protect the mental health of this staff cohort, as well as encouraging resilience and successful coping among staff themselves.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".