Acceptability and feasibility of recruitment and data collection in a field study of hospital nurses’ handoffs using mobile devices
Bibliographic record
Abstract
BACKGROUND: The portability and multiple functionalities of mobile devices make them well suited for collecting field data for naturalistic research, which is often beset with complexities in recruitment and logistics. This paper describes the implementation of a research protocol using mobile devices to study nurses' exchanges of patient information at change of shift. METHODS: Nurses from three medical and surgical units of an acute care teaching hospital in Montreal, Canada, were invited to participate. On 10 selected days, participants were asked to record their handoffs using mobile devices and to complete paper questionnaires regarding these exchanges. Nurse acceptance of mobile devices was assessed using a 30-item technology acceptance questionnaire and focus group interviews. The principal feasibility indicator was whether or not 80 complete handoffs could be collected on each unit. RESULTS: From October to December 2017, 63 of 108 eligible nurses completed the study. Results suggest that the use of mobile devices was acceptable to nurses, who felt that the devices were easy to use but did not improve their job performance. The principal feasibility criterion was met, with complete data collected for 176, 84, and 170 of the eligible handoffs on each unit (81% of eligible handoffs). The research protocol was acceptable to nurses, who felt the study's demands did not interfere with their clinical work. CONCLUSIONS: The research protocol involving mobile devices was feasible and acceptable to nurses. Nurses felt the research protocol, including the use of mobile devices, required minimal investment of time and effort. This suggests that their decision to participate in research involving mobile devices was based on their perception that the study protocol and the use of the device would not be demanding. Further work is needed to determine if studies involving more sophisticated and possibly more demanding technology would be equally feasible and acceptable to nurses.
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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 | 0.239 | 0.282 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".