Supporting work practices through telehealth: impact on nurses in peripheral regions
Bibliographic record
Abstract
BACKGROUND: In Canada, workforce shortages in the health care sector constrain the ability of the health care system to meet the needs of its population and of its health care professionals. This issue is of particular importance in peripheral regions of Quebec, where significant inequalities in workforce distribution between regions has lead to acute nursing shortages and increased workloads. Information and communication technologies (ICTs) are innovative solutions that can be used to develop strategies to optimise the use of available resources and to design new nursing work practices. However, current knowledge is still limited about the real impact of ICTs on nursing recruitment and retention. Our aim is to better understand how work practice reorganization, supported by ICTs, and particularly by telehealth, may influence professional, educational, and organizational factors relating to Quebec nurses, notably those working in peripheral regions. METHODS/DESIGN: First, we will conduct a descriptive study on the issue of nursing recruitment. Stratified sampling will be used to select approximately twenty innovative projects relating to the reorganization of work practices based upon ICTs. Semi-structured interviews with key informants will determine professional, educational, and organizational recruitment factors. The results will be used to create a questionnaire which, using a convenience sampling method, will be mailed to 600 third year students and recent graduates of two Quebec university nursing faculties. Descriptive, correlation, and hierarchical regression analyses will be performed to identify factors influencing nursing graduates' intentions to practice in peripheral regions. Secondly, we will conduct five case studies pertaining to the issue of nursing retention. Five ICT projects in semi-urban, rural, and isolated regions have been identified. Qualitative data will be collected through field observation and approximately fifty semi-structured interviews with key stakeholders. DISCUSSION: Data from both parts of this research project will be jointly analysed using triangulation of researchers, theoretical approaches, methods, and results. Continuous exchanges with decision makers and periodic knowledge transfer activities are planned to facilitate the dissemination and utilization of research results in policies regarding the nursing recruitment and retention.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".