Determinants of Primary Care Nurses’ Intention to Adopt an Electronic Health Record in Their Clinical Practice
Bibliographic record
Abstract
A provincial electronic health record is being developed in the Province of Quebec (and in all other provinces in Canada), and authorities hope that it will enable a safer and more efficient healthcare system for citizens. However, the expected benefits can occur only if healthcare professionals, including nurses, adopt this technology. Although attention to the use of the electronic health record by nurses is growing, better understanding of nurses' intention to use an electronic health record is needed and could help managers to better plan its implementation. This study examined the factors that influence primary care nurses' intention to adopt the provincial electronic health record, since intention influences electronic health record use and implementation success. Using a modified version of Ajzen's Theory of Planned Theory of Planned Behavior, a questionnaire was developed and pretested. Questionnaires were distributed to 199 primary care nurses. Multiple hierarchical regression indicated that the Theory of Planned Behavior variables explained 58% of the variance in nurses' intention to adopt an electronic health record. The strong intention to adopt the electronic health record is mainly determined by perceived behavioral control, normative beliefs, and attitudes. The implications of the study are that healthcare managers could facilitate adoption of an electronic health record by strengthening nurses' intention to adopt the electronic health record, which in turn can be influenced through interventions oriented toward the belief that using an electronic health record will improve the quality of patient care.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".