Bibliographic record
Abstract
The participation of senior nursing healthcare executives in the acquisition of electronic healthcare information system is not well understood. This is an important issue because nurses make up the majority of care-providers within the Canadian healthcare system, and thus the majority of the information systems end-users. End-user involvement in the selection and evaluation of a healthcare information system is vital to implementation success; it is very important we understand the participation of the nursing leadership making these important decisions. The purpose of this quantitative study was to explore this gap in our understanding, to find ‘Nursing’s Voice’ in this process. The soft-systems methodology theoretical perspective was used to understand how this process might be improved. Senior healthcare executives with a background in nursing from each of the Health Authorities across British Columbia were recruited to participate in an online survey questionnaire. An N=11 of senior executives were invited to participate, and a response rate of 82% was achieved. The results showed that despite a lack of formal training in information technology subjects, the majority of these nursing leaders do take an active role in electronic healthcare information systems acquisition and upgrading projects along-side their health informatics colleagues; ‘Nursing’s Voice’ is clearly heard.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 | 0.086 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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".