A Qualitative Content Analysis of Nurses’ Comfort and Employment of Workarounds With Electronic Documentation Systems in Home Care Practice
Bibliographic record
Abstract
Background Electronic documentation systems have the potential to assist registered nurses with timely access to patient health- and care-related information. Registered nurses are the largest users of electronic documentation systems; however, limited evidence exists about their comfort with electronic documentation system usage and the types of workarounds developed within the context of home care. Aim To explore home care registered nurses’ comfort with electronic documentation system usage and identify the types and reasons for the development and implementation of workarounds. Methods A cross-sectional survey design was employed to collect quantitative and qualitative data. A total of 217 home care registered nurses participated in the survey. Quantitative data were analyzed using descriptive statistics. Qualitative data were analyzed using inductive content analysis. Findings: Individual (e.g., registered nurses’ technology-related experience), technological (e.g., electronic documentation system design) and organizational (e.g. training) characteristics influenced registered nurses’ comfort with electronic documentation system usage. Furthermore, workarounds stemmed from the technological characteristics of the electronic documentation system. Conclusion Findings highlight the need for assessing registered nurses’ level of comfort with electronic documentation system usage to inform training initiatives. Including registered nurses in the system design is advocated to ensure electronic documentation systems fit with the complexity of nursing practice, potentially enhancing registered nurses’ level of comfort and mitigating the development and employment of workarounds during system usage.
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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 | 0.014 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".