Public Health Nursesʼ Perceptions of Mobile Computing in a School Program
Bibliographic record
Abstract
The use of mobile computing (MC) in healthcare practice has grown substantially in recent years, yet little is known about its impact. This descriptive, exploratory, qualitative study explored the perceptions of public health nurses (PHNs) in a school health program about their use of MC. Public health nurses participated in focus group interviews and completed weekly reflections. They perceived that MC (a) increased PHNs' flexibility although they were constrained by work rules, (b) increased peer and employer connectedness yet increased isolation, (c) and increased PHNs' status while creating a wider gap between PHNs and their clients. Public health nurses described their practice as being more efficient and client-focused with MC. Over time, PHNs grew more comfortable with the tool, developed a dependence on it, and learned to deal with technological problems. Although this new technology shows promise, there is a need for further research to examine its impact as a tool to promote public health nursing practice.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".