Service process factors affecting patients’ and clinicians’ experiences on rapid teleconsultation implementation in out-patient neurology services during COVID-19 pandemic: a scoping review
Bibliographic record
Abstract
BACKGROUND: The adoption of teleconsultation for outpatient neurology services was limited until the onset of the COVID-19 pandemic which forced many outpatient neurology services to rapidly switch to virtual models. However, it remains unclear how this change has impacted patients' and clinicians' perceptions of service quality. The purpose of this scoping review is to identify process factors that influence patients' and clinicians' experiences of outpatient teleconsultation services during COVID-19. METHODS: Arksey and O'Malley scoping review framework was used to search PubMed, Scopus, CINAHL, and PsycInfo for original peer-reviewed research studies that examined the experiences of synchronous teleconsultation between a clinician and patient in a home-setting since the World Health Organization announced the COVID-19 global pandemic. The service quality model SERVQUAL was used to conduct a deductive thematic analysis to identify the key factors that impacted the patients' and clinicians' perception of teleconsultation services. RESULTS: A total of nineteen studies published between January 1, 2020, and April 17, 2021, were identified. The most common service process factors affecting the patients' and clinicians' experiences of teleconsultation were technical issues, addressing logistical needs, communication, ability to perform clinical activities, appropriate triage, and administrative support. CONCLUSIONS: Our findings identified six key service process factors affecting the patients' and clinicians' teleconsultation experiences in outpatient neurology services. The need for improvement of triage process and standardizing administrative virtual care pathway are identified as important steps to improve patients and clinicians' teleconsultation experiences compared to pre-COVID era. More research is needed to assess outpatient neurology teleconsultation service quality from patients' and clinicians' perspectives.
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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 | 0.026 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.009 | 0.014 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".