Physical and occupational therapists' learning needs and preferences for education on functional electrical stimulation: A qualitative descriptive study
Bibliographic record
Abstract
BACKGROUND: Functional electrical stimulation (FES) is an evidence-based intervention that is rarely used by Canadian physical therapists (PTs) and occupational therapists (OTs). A common barrier to FES implementation is a lack of knowledge and training. FES learning resources that meet therapists' learning needs and preferences may address this barrier. OBJECTIVE: To explore OTs' and PTs' perspectives on the utility of FES e-learning resources, including whether the resources met their learning needs and preferences. METHODS: Through this qualitative descriptive study, feedback on FES e-learning resources was solicited from 5 PTs and 18 OTs who work clinically, but do not use FES. Participants reviewed ≥3 prerecorded lectures on FES topics and then completed a semi-structured interview. Participants were asked about the lectures' content, delivery, ease of use and comprehensibility, and were asked to suggest modifications to facilitate self-directed learning about FES. Interviews were analyzed using conventional content analysis. RESULTS: Three themes were identified. (1) Feedback on FES e-learning: the content and delivery of the prerecorded lectures facilitated participants' learning, although opportunities to improve content and delivery were identified. (2) Factors influencing FES learning needs and preferences: Participants identified internal (i.e., baseline knowledge, learning style) and external (i.e., learning with colleagues, practice setting) factors that affected learning. Themes 1 and 2 impacted (3) the effects of FES e-learning: Participants described the outcomes of their FES e-learning, including increases in FES knowledge, confidence, and use in clinical practice. CONCLUSION: Therapists' perceived the FES e-learning resources to be useful and to address their learning needs and preferences.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".