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Record W6958382449 · doi:10.6084/m9.figshare.12850562

Using remote learning to teach clinicians manual wheelchair skills: a cohort study with pre- vs post-training comparisons

2020· article· en· W6958382449 on OpenAlexaff

Bibliographic record

VenueFigshare · 2020
Typearticle
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsDalhousie University
Fundersnot available
KeywordsWheelchairTest (biology)TrainerCurriculumSession (web analytics)Intervention (counseling)Dreyfus model of skill acquisitionCohortCLIPSRehabilitation

Abstract

fetched live from OpenAlex

To test the hypothesis that remote learning to teach clinicians manual wheelchair skills is efficacious. A convenience sample of therapists (physical and occupational) and students were enrolled in pairs in a cohort study with pre- versus post-training comparisons. The intervention was a hybrid of self-study and hands-on practice paired with remote feedback for ten intermediate and advanced manual wheelchair skills. Participants practiced with self-selected frequency and duration, uploading a session log and video(s) to an online platform. A remote trainer provided asynchronous feedback prior to the next practice session. Capacity and confidence in completing the ten skills were evaluated using the Wheelchair Skills Test Questionnaire (WST-Q). Knowledge of wheelchair skills training and motor learning was assessed using a 62-item Knowledge Test. Secondary outcome measures included skill achievement, as confirmed by submitted video recordings, and participant feedback about the training. Across 41participants, scores were higher at follow-up compared to baseline for WST-Q capacity (73.9 ± 19.1 vs 16.8 ± 15.6, p < 0.001), WST-Q confidence (80.1 ± 12.2 vs 47.6 ± 18.2, p = 0.003) and knowledge (70.8 ± 7.5 vs 67.0 ± 5.4, p = 0.004). Remote learning can increase wheelchair skills capacity and confidence as well as knowledge about such training and assessment. This model should be further investigated as a delivery method for training rehabilitation professionals. NCT01807728.Implications for rehabilitationWheelchair skills training is one of the 8 steps of wheelchair provision as outlined by the World Health Organization.Wheelchair skills are not a core part of most clinical curriculums and many clinicians cite a lack of resources and uncertainty on how to implement wheelchair skills training into practice as major barriers to providing such training.Remote learning offers the benefits of structured wheelchair skills training with expert feedback on an individual’s own schedule that is not afforded by one-day “bootcamp”-type courses or on-the-job training, which are how many clinicians currently learn wheelchair skills.In a sample of physical and occupational therapists and students, remote learning was effective at increasing capacity and confidence to perform manual wheelchair skills as well as knowledge of wheelchair training. Wheelchair skills training is one of the 8 steps of wheelchair provision as outlined by the World Health Organization. Wheelchair skills are not a core part of most clinical curriculums and many clinicians cite a lack of resources and uncertainty on how to implement wheelchair skills training into practice as major barriers to providing such training. Remote learning offers the benefits of structured wheelchair skills training with expert feedback on an individual’s own schedule that is not afforded by one-day “bootcamp”-type courses or on-the-job training, which are how many clinicians currently learn wheelchair skills. In a sample of physical and occupational therapists and students, remote learning was effective at increasing capacity and confidence to perform manual wheelchair skills as well as knowledge of wheelchair training.

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)0.003
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 score0.005
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.152
GPT teacher head0.437
Teacher spread0.285 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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