Evaluating the validity of a functional electrical stimulation clinical decision making tool: A qualitative study
Bibliographic record
Abstract
Introduction: Following central nervous system damage, the recovery of motor function is a priority. For some neurological populations, functional electrical stimulation (FES) is recommended in best practice guidelines for neurorehabilitation. However, limited resources exist to guide FES application, despite clinicians reporting that a lack of FES knowledge prevents use in clinical practice. The FES Clinical Decision Making Tool was developed to assist clinicians with FES application and translation into clinical practice. The purpose of this study was to evaluate the content validity of the Tool from the perspectives of Canadian physical and occupational therapists using FES in neurorehabilitation. Methods: Thirteen participants (twelve women, one man), aged 40.5 ± 10.3 years, participated in individual semi-structured interviews to explore their clinical decision making experiences when applying FES and to evaluate the content validity (i.e., appropriateness, comprehensibility, and comprehensiveness) of the Tool. Interviews were analyzed using a qualitative conventional content analysis following the DEPICT model. Results: Three themes were identified. 1) Clinician context influences FES usage. Participants' experiences with FES use varied and application was influenced by treatment goals. 2) Parameter selection in clinical practice. Participants identified decision-making strategies and the challenges of parameter selection. 3) With modifications, the Tool is a valid resource to inform FES applications. Participants discussed its strengths, limitations, and suggested changes. While the Tool is useful, a more extensive resource (e.g., appendix) for the Tool is warranted. Discussion: A revised Tool was created to improve its comprehensiveness and comprehensibility. Thus, the Tool is a valid resource for applying FES in neurorehabilitation.
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 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 | 0.080 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".