Experiences of case and contact management teams during the COVID-19 pandemic response in Ontario
Bibliographic record
Abstract
Case and contact management (CCM) teams play a vital role in the COVID-19 pandemic response. This study aims to understand the experiences of CCM team members during the COVID-19 pandemic to identify areas of improvement for future crises. A mixed-methods, cross-sectional online survey was conducted from November 2020 to March 2021. There were a total of 49 relevant survey responses. Participants were CCM team members responding to the pandemic in Ontario. Frequency tabulations were used to analyze closed-ended survey responses, and both a conventional content analysis and thematic analysis were conducted on the open-ended responses. Study results revealed that inadequate planning and preparedness, poor communication, high workloads, and stress levels of CCM team members were notable areas of concern. These matters ultimately affected the well-being of CCM team members and acted as barriers to completing CCM work. It is imperative that adequate staffing and accessibility to mental health support from employers are improved in future times of crisis to ensure that CCM teams are able to meet the demands of their work. Further studies should be conducted to examine the experiences of CCM team members as well as barriers and facilitators to completing CCM 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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".