Nursing practice and teleconsultations in a pandemic context: A mixed‐methods study
Bibliographic record
Abstract
AIM: To explore the use and implementation of teleconsultations by primary care nurses in the context of the COVID-19 pandemic. BACKGROUND: Teleconsultation use increased rapidly during the COVID-19 pandemic. Its implementation has been documented for physicians and specialists, but knowledge is still limited in nursing practice. DESIGN: A sequential mixed-methods study. METHODS: Phase 1: A cross-sectional e-survey with 98 nurses (64 nurse clinicians [NCs] and 34 nurse practitioners [NPs]) was conducted in 2020 in 48 teaching primary care clinics in Quebec (Canada). Phase 2: Semi-structured interviews with four NCs and six NPs were conducted in 2021 in three primary care clinics. This study adheres to STROBE and COREQ guidelines. RESULTS: During the pandemic, telephone was the principal teleconsultation modality used by NPs and NCs compared to other teleconsultation modalities (text messages, email and video). The only variable associated with a higher likelihood of using teleconsultations was type of professional (NCs). Video consultation was almost absent from the modalities used. The majority of participants reported several facilitators to using teleconsultations in their work (e.g. web platforms and work-family balance) and for patients (e.g. rapid access). Some barriers to utilisation were identified (e.g. lack of physical resources) for successful integration of teleconsultations at the organisational, technological and systemic levels. Participants also reported positive (e.g. assessment of cognitive deficiency) and negative (e.g. rural population) impacts of using teleconsultations during a pandemic that made the use of teleconsultations complex. CONCLUSION: This study highlights the potential for nurses to use teleconsultations in primary care practice and suggests concrete solutions to encourage their implementation after the pandemic. RELEVANCE TO CLINICAL PRACTICE: Findings emphasize the need for updated nursing education, easy-to-use technology and the strengthening of policies for the sustainable use of teleconsultations in primary health care. IMPLICATIONS FOR THE PROFESSION: This study could promote the sustainable use of teleconsultations in nursing practice. REPORTING METHOD: The study adhered to relevant EQUATOR guidelines; the STROBE checklist for cross-sectional studies and the COREQ guidelines for qualitative studies were used for reporting. PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution, as the study focused on the use of teleconsultation among health professionals, specifically primary care nurses.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".