Valuing tacit nursing knowledge during the COVID‐19 pandemic
Bibliographic record
Abstract
Public health nurses in Ontario, Canada, support the healthy growth and development of children across the province through a variety of programs including home visits for pregnant individuals and families with young children. During the COVID-19 global pandemic the needs of families increased while access to health and social services decreased. During this time, home visiting teams closely involved in supporting families also experienced staff redeployment to support pandemic efforts (e.g., case and contact management, vaccinations) and changes to the nature of home visiting work, including shifts to remote or virtual service delivery. To support nursing practice in this new and evolving context, a framework for capturing and sharing the tacit or how-to knowledge of public health nurses was developed. A valuing of this type of knowledge for informing future public health nursing practice - well beyond the pandemic response - was recognized as a pandemic silver lining when reflecting on two years of supporting home visiting teams in our province.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.023 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".