Listening to School Nurses' Voices: A Mixed Methods Study on the Continued Impact of COVID-19 on School Nursing Practice
Bibliographic record
Abstract
School closures in March 2020 due to the COVID-19 pandemic precipitated losses of critical student resources as physical, mental, emotional, and social needs escalated. Identifying the challenges, strategies, and changes in school nurse (SN) practice in Massachusetts during this pandemic is fundamental to understanding how to manage future anticipated pandemics while protecting children, communities, and SNs. The purpose of this mixed-methods descriptive study in the second year of the global pandemic was to (a) listen to SN voices through a novel online survey including the prompts of challenges, strategies, and practice changes and (b) describe the SN experience of COVID-19 response in Massachusetts schools, including identification of intent to leave school nursing. Responses were analyzed using descriptive qualitative analysis ( n = 73). The prompts each elicited subthemes that coalesced to a cohesive theme: Finding one's way required the support of others to pave untraversed roads.
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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 | 0.022 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".