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Record W4410151992 · doi:10.2196/71950

Barriers to and Facilitators of Implementing Overnight Nursing Teleconsultation in Small, Rural Long-Term Care Facilities: Qualitative Interview Study

2025· article· en· W4410151992 on OpenAlexaffvenueabout
Véronique Nabelsi, Véronique Plouffe, Marie Chantal Leclerc

Bibliographic record

VenueJMIR Aging · 2025
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsUniversité du Québec en OutaouaisUniversité TÉLUQUniversité du Québec à Montréal
Fundersnot available
KeywordsNursingWorkloadMedicinePandemicNursing shortageFamily medicineCoronavirus disease 2019 (COVID-19)Nurse education

Abstract

fetched live from OpenAlex

BACKGROUND: Teleconsultation has expanded rapidly in recent years, especially during the COVID-19 pandemic, and has become standard practice among physicians. The benefits of teleconsultation, namely, improving access to care, ensuring continuity and quality of care, increasing patient satisfaction, and reducing costs and wait times, are well documented. However, its use in nursing practice, especially in long-term care settings, remains underresearched despite its significant transformative potential, particularly in resource-limited and rural settings, where it could address major challenges such as nursing shortages and access to care. OBJECTIVE: This study aimed to identify barriers to and facilitators of implementing overnight nursing teleconsultation in rural residential and long-term care centers in Quebec, Canada (centres d'hébergement et de soins de longue durée [CHSLDs]), with ≤50 beds. METHODS: A 6-month pilot project was rolled out sequentially in 3 rural CHSLDs in 2 administrative regions of Quebec between July 2022 and March 2023. A total of 38 semistructured interviews were conducted with managers (n=27, 71%), nursing staff members (n=9, 24%), and resident committee presidents (n=2, 5%) between February 2023 and July 2023. RESULTS: The study identified several barriers to the implementation of teleconsultation. The main barriers reported included union opposition (managers: 23/27, 85%), network instability (resident committee presidents: 2/2, 100%), limited technology skills (nursing staff members: 7/9, 78%), a perceived increase in workload (nursing staff members: 8/9, 89%; resident committee presidents: 2/2, 100%), and a low volume of teleconsultations (nursing staff members: 8/9, 89%). Despite the barriers, participants also identified key facilitators. These included the care setting (nursing staff members: 9/9, 100%; managers: 21/27, 78%), buy-in from senior management and managers (managers: 27/27, 100%; resident committee presidents: 2/2, 100%), collaboration between the departments (nursing staff members: 9/9, 100%), nursing staff motivation (nursing staff members: 9/9, 100%), and improvements in professional practices (nursing staff members: 8/9, 89%). Finally, the relative benefits of teleconsultation, such as enhanced mutual vision, faster assessment of clinical situations, improved resident care management quality, and greater flexibility and safety, were unanimously recognized (38/38, 100%) as contributing to its acceptability and potential for success. CONCLUSIONS: This study provides an in-depth understanding of the barriers to and facilitators of implementing overnight nursing teleconsultation in small rural CHSLDs. This constitutes a sound basis for developing tailored strategies aimed at overcoming identified barriers and optimizing facilitators. The results also provide practical guidelines for decision makers, highlighting the need to adapt implementation approaches to the unique context of each facility. Furthermore, this study highlights the importance of further research to broaden our knowledge on the dissemination and scale-up of health care innovations. This includes the development of learning health systems capable of responding in an agile and effective way to the needs of rural and vulnerable populations both in Quebec and elsewhere.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.224
Threshold uncertainty score0.628

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.033
GPT teacher head0.430
Teacher spread0.397 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2025
Admission routes3
Has abstractyes

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