The Forgotten (Invisible) Healthcare Heroes: Experiences of Canadian Medical Laboratory Employees Working During the Pandemic
Bibliographic record
Abstract
Objective: The purpose of this qualitative study was to understand the stressors and coping strategies of medical laboratory technologists (MLTs) and assistants (MLAs) working during the COVID-19 pandemic in Ontario, Canada. Methods: In this descriptive qualitative study, we held two focus groups with MLTs and MLA who were working during the COVID-19 pandemic. The focus group sessions were transcribed verbatim followed by thematic analysis to develop codes and themes. Findings: = 6) MLA. Overall, the stressors and coping methods identified between both focus groups were consistent. Our results revealed four main themes: (1) COVID-19 contributing to the notable and existing staff shortage; (2) the pandemic reinforced that medical laboratory employees are forgotten within the healthcare system; (3) a poor work environment exacerbated by the pandemic; and (4) a resilient and passionate group. Rich descriptions explained the underlying issues related to the themes. Conclusions: MLTs and MLAs are critical members of the healthcare team and provide vital patient care services. This study explored their experiences working during the pandemic and offers timely recommendations to mitigate against occupational stressors.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.027 | 0.019 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 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".