Hidden and Understaffed: Exploring Canadian Medical Laboratory Technologists’ Pandemic Stressors and Lessons Learned
Bibliographic record
Abstract
(1) Background: The COVID-19 pandemic has highlighted the critical role of medical laboratory technologists (MLTs) in the healthcare system. Little is known about the challenges MLTs faced in keeping up with the unprecedented demands posed by the pandemic, which contributed to the notable staff shortage in the profession. This study aims to identify and understand the stressors of MLTs in Canada and the lessons learned through their lived experiences during the pandemic. (2) Methods: In this descriptive qualitative study, we conducted five semi-structured focus groups with MLTs working during the pandemic. The focus group sessions were audio-recorded and then transcribed verbatim. Thematic analysis was used to inductively code data and identify themes. (3) Results: A total of 27 MLTs across Canada participated in the study. Findings highlighted four key themes: (i) unexpected challenges navigating through the uncertainties of an ever-evolving pandemic; (ii) implications of staff shortage for the well-being of MLTs and quality of patient care; (iii) revealing the realities of the hidden, yet indispensable role of MLTs in predominantly non-patient-facing roles; and (iv) leveraging insights from the COVID-19 pandemic to enhance healthcare practices and preparedness. (4) Conclusion: The study provides in-depth insight into the experiences of MLTs across Canada during the pandemic. Based on our findings, we provide recommendations to enhance the sustainability of the laboratory workforce and ensure preparedness and resiliency among MLTs for future public health emergencies, as well as considerations as to combating the critical staff shortage.
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.037 | 0.017 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".