[The impact of information and communication technologies on nurse retention].
Bibliographic record
Abstract
AIM: The purpose of this study was to determine the potential impact of information and communication technologies (ICTs) on nursing practice and nurse retention in remote, intermediate and peripheral regions of Quebec, Canada. METHODS: A qualitative study using semi-structured interviews (n=21) was conducted among nursing managers from 16 health and social services centers (French acronym: CSSS) in the province of Quebec. RESULTS: The study found that a range of ICT applications are used, though not to the same extent in all organizations. The participants assessed the impact of computerization and telehealth applications on nursing practice and emphasized the relationships between telehealth and nurse retention, particularly through professional development. The participants also reported that ICTs can have different impacts on nurse retention (i.e. little or no impact, unclear impact, or indirect positive impact). CONCLUSIONS: The main findings indicate significant heterogeneity, both in terms of the nursing shortage and in terms of the integration of ICT in nursing practice. While focusing on a comparative approach, future research should further explore the impact of ICT on nursing practice and, indirectly, on nurse retention, which requires a contextual approach to ICT applications and workplaces and an analysis of staff characteristics.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".