The Influence of a Wound Care Teleassistance Service on Nursing Practice: A Case Study in Quebec
Bibliographic record
Abstract
BACKGROUND: Although telehealth is a promising solution for healthcare professionals who work in remote and rural regions, the influence of specific telehealth applications on the nursing workforce remains unknown. This case study aimed to explore the potential influence of a teleassistance service in wound care (the acronym in French is TASP) on nursing practices and on nurse retention in peripheral areas. MATERIALS AND METHODS: We carried out an exploratory single case study based on 16 semistructured interviews with two promoters of TASP, five nursing managers, and nine nurses from three levels of expertise associated with this service. RESULTS: According to participants, the main positive influences of TASP were observed in quality of care, professional autonomy, professional development, and decrease of professional isolation. Participants mentioned increased workload associated with global patient data collection at first consultation as a negative effect of TASP. Finally, three possible effects of TASP on nurse retention were identified: none or minimal, imprecise, or mostly positive. CONCLUSIONS: This case study highlights the positive influence of TASP on several dimensions of nursing practice, in addition to its essential role in improving the quality of care. However, it is important to consider that the service cannot be considered as a solution to or replacement for the shortage of nurses.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".