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Record W4282941034 · doi:10.3138/ptc-2022-0092

Canadian Physiotherapists Integrate Virtual Care during the COVID-19 Pandemic

2022· article· en· W4282941034 on OpenAlexaffvenueabout
Allison M Ezzat, Jean-François Esculier, Sarah Lord Ferguson, Christopher Napier, Sabrina T. Wong

Bibliographic record

VenuePhysiotherapy Canada · 2022
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsSimon Fraser UniversityRunning Injury ClinicUniversity of British Columbia
Fundersnot available
KeywordsThematic analysisPsychological interventionDescriptive statisticsNursingPandemicMedicineVirtual patientPsychologyCoronavirus disease 2019 (COVID-19)Qualitative research

Abstract

fetched live from OpenAlex

Purpose: To examine Canadian physiotherapists' experiences in adapting their delivery of patient care during the COVID-19 pandemic. We examine the level of strain on the profession and barriers and enablers to virtual care and provide strategies to support future virtual care implementation. Methods: From May to October 2020, a series of eight cross-sectional survey cycles were distributed every 2-4 weeks through branches and divisions of the Canadian Physiotherapy Association, social media, and personal networks. Descriptive statistics summarized the main findings. Open ended questions were first analyzed inductively using thematic analysis, then deductively mapped to the Capability-Opportunity-Motivation Behavioural (COM-B) Model. Results: Between 1,820 (cycle 1) and 334 (cycle 7) physiotherapists responded. Median strain level was 5/5 (cycle 1) and dropped to median 3/5 (cycles 5-8). In cycle 1, 55% of physiotherapists had ceased in-person care, while 41% were providing modified in-person care. Of these physiotherapists, 79% were offering virtual care. As modified in-person care increased, virtual care continued as a substantial aspect of practice. Physiotherapists identified barriers (e.g., lack of hands-on care) and enabling factors (e.g., greater accessibility to patients) for virtual care. In-depth examination of the barriers and enablers through the COM-B lens identified potential interventions to support future virtual care implementation, including education and training resources for physiotherapists and communication and advocacy to patients and the public on the value of virtual care. Conclusions: Canadian physiotherapists exhibited high adaptability in response to COVID-19 through the rapid and widespread use of virtual care. By creating an in-depth understanding of the barriers and enablers to virtual care, along with potential interventions, this work will facilitate future opportunities to support and enhance physiotherapists' delivery of virtual care.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.075
Threshold uncertainty score0.288

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.002
Scholarly communication0.0030.001
Open science0.0020.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.018
GPT teacher head0.330
Teacher spread0.311 · 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 source (direct Gemma or distilled Codex), 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

Citations14
Published2022
Admission routes3
Has abstractyes

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