Analysis of Private Physiotherapy Clinic Web Sites Using a Critical Perspective
Bibliographic record
Abstract
Purpose: The objective of our study was to analyze visual and textual content of private physiotherapy clinic Web sites with a critical analysis framework. Method: We analyzed 43 private physiotherapy clinics' Web sites from all regions of one Canadian province (Quebec). For each Web site, we collected and aggregated the data using a standardized extraction grid to index visual and textual content. We then conducted an analysis of the collected data using the Seven-Step Framework for Critical Analysis proposed by Nixon and colleagues. Results: Most Web sites presented elements related to sports and active lifestyles in their names, logos, or pictures. Persons represented in the Web sites were mainly young, white, and active. Ethnic and body diversity were generally not depicted. Information encompassing manual therapy and sports injuries management largely prevailed. Conclusions: The textual and visual content of private physiotherapy clinic Web sites was not consistent with the physiotherapy community's commitments to upholding equity principles and to serving a wide range of individuals. To fulfill the highest professional and ethical standards, the physiotherapy community should reflect on the representation of physiotherapy services and clients on Web sites to ensure that the trend towards privatization of physiotherapy services does not perpetuate the systems of inequality present in society.
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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 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.018 | 0.014 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".