<i>Please help us</i> : Canadian childcare providers’ calls for aid during the COVID-19 pandemic
Bibliographic record
Abstract
COVID-19 poses serious challenges to the health and safety of childcare providers, and these challenges were particularly acute in early 2022 during the first Omicron wave when vaccines were not available for children. Childcare providers work in environments where the recommended methods to minimize COVID-19 infection were often not possible to implement: children could not wear masks for extended periods and were not able to maintain physical distance. Under these pressures, Canada’s already-fragile childcare sector was strained, caregivers struggled, and existing deficiencies were exacerbated. As part of a larger quality assurance and improvement project examining the impacts of the pandemic on childcare in the Canadian prairie province of Manitoba, this paper reports on qualitative findings to make public health and policy recommendations for the childcare sector. Data were voluntarily provided by a sample of childcare providers between January 6–13, 2022. A thematic analysis of open-text responses was performed, finding: an urgent need for health and safety resources; a need for sustained support and guidelines; and an absence of value and respect. We also identified an emergent theme of gratitude, which was reflected by an overwhelming number of thanks to the project team for listening to the voices of childcare providers. We draw on our qualitative analysis to propose measures to improve public health and safety, funding, and infrastructure, as well as to underscore the importance of systematic data collection and evaluation to protect and support the essential childcare workforce through the COVID-19 pandemic and into the future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.025 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".