The mental health of laboratory and rehabilitation specialists during COVID-19: A rapid review
Bibliographic record
Abstract
Backgrounds: Healthcare workers have experienced considerable stress and burnout during the COVID-19 pandemic. Among these healthcare workers are medical laboratory professionals and rehabilitation specialists, specifically, occupational therapists, and physical therapists, who all perform critical services for the functioning of a healthcare system. Purpose: This rapid review examined the impact of the pandemic on the mental health of medical laboratory professionals (MLPs), occupational therapists (OTs) and physical therapists (PTs) and identified gaps in the research necessary to understand the impact of the pandemic on these healthcare workers. Methods: We systematically searched "mental health" among MLPs, OTs and PTs using three databases (PsycINFO, MEDLINE, and CINAHL). Results: Our search yielded 8887 articles, 16 of which met our criteria. Our results revealed poor mental health among all occupational groups, including burnout, depression, and anxiety. Notably, MLPs reported feeling forgotten and unappreciated compared to other healthcare groups. In general, there is a dearth of literature on the mental health of these occupational groups before and during the pandemic; therefore, unique stressors are not yet uncovered. Conclusions: Our results highlight poor mental health outcomes for these occupational groups despite the dearth of research. In addition to more research among these groups, we recommend that policymakers focus on improving workplace cultures and embed more intrinsic incentives to improve job retention and reduce staff shortage. In future emergencies, providing timely and accurate health information to healthcare workers is imperative, which could also help reduce poor mental health outcomes.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".