The Current State of Remote Physiotherapy in Finland: Cross-sectional Web-Based Questionnaire Study
Bibliographic record
Abstract
BACKGROUND: The ongoing COVID-19 pandemic has required social, health, and rehabilitation organizations to implement remote physiotherapy (RP) as a part of physiotherapists' daily practice. RP may improve access to physiotherapy as it delivers physiotherapy services to rehabilitees through information and communications technology. Even if RP has already been introduced in this century, physiotherapists' opinion, amount of use, and form in daily practice have not been studied extensively. OBJECTIVE: This study aims to investigate physiotherapists' opinions of the current state of RP in Finland. METHODS: A quantitative, cross-sectional, web-based questionnaire was sent to working-aged members of the Finnish Association of Physiotherapists (n=5905) in March 2021 and to physiotherapists in a private physiotherapy organization (n=620) in May 2021. The questionnaire included questions on the suitability of RP in different diseases and the current state and implementation of RP in work among physiotherapists. RESULTS: Of the 6525 physiotherapists, a total of 9.9% (n=662; n=504, 76.1% female; mean age 46.1, SD 12 years) answered the questionnaire. The mean suitability "score" (0=not suitable at all to 10=fully suitable) of RP in different disease groups varied from 3.3 (neurological diseases) to 6.1 (lung diseases). Between early 2020 (ie, just before the COVID-19 pandemic) and spring 2021, the proportion of physiotherapists who used RP increased from 33.8% (21/62) to 75.4% (46/61; P<.001) in the public sector and from 19.7% (42/213) to 76.6% (163/213; P<.001) in the private sector. However, only 11.7% (32/274) of physiotherapists reported that they spent >20% of their practice time for RP in 2021. The real-time method was the most common RP method in both groups (public sector 46/66, 69.7% vs private sector 157/219, 71.7%; P=.47). The three most commonly used technical equipments were computers/tablets (229/290, 79%), smartphones (149/290, 51.4%), and phones (voice call 51/290, 17.6%). The proportion of physiotherapists who used computers/tablets in RP was higher in the private sector than in the public sector (183/221, 82.8% vs 46/68, 67.6%; P=.01). In contrast, a higher proportion of physiotherapists in the public sector than in the private sector used phones (18/68, 26.5% vs 33/221, 14.9%; P=.04). CONCLUSIONS: During the COVID-19 pandemic, physiotherapists increased their use of RP in their everyday practice, although practice time in RP was still low. When planning RP for rehabilitees, it should be considered that the suitability of RP in different diseases seems to vary in the opinion of physiotherapists. Furthermore, our results brought up important new information for developing social, health, and rehabilitation education for information and communications technologies.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".