Physical Therapy and Social Media: Protocol for a Critical Interpretive Synthesis of Ethical and Social Issues
Bibliographic record
Abstract
OBJECTIVE: This study aims to critically review existing literature and develop a theory to understand the ethical and social issues related to social media uses by physical therapists. METHODS: The review will follow the Critical Interpretive Synthesis method by Dixon-Woods et al. Databases such as Medline, Embase, CINAHL, and Web of Science will be searched for studies published in English and French from 2000 to 2025. Two reviewers will select studies, extracting and analyzing data. Ethical and social issues will be critically appraised, and new concepts will be synthesized. DISCUSSION: This review critically examines ethical and social issues in the use of social media in physical therapy, highlighting gaps in literature and guiding future research. Despite some methodological limitations, it offers a nuanced, context-sensitive synthesis. The findings aim to support ethical reflection in physical therapy and related fields. JOSPT Methods 2025;1(3):96-101. Epub 16 July 2025. doi:10.2519/josptmethods.2025.0134
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
| grok | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
| opus | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Systematic review | medium |
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 | 0.194 | 0.210 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.073 | 0.019 |
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, unvalidatedLabeled directly by 3 models reading the full record.
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".