Seven-Step Framework for Critical Analysis and Its Application in the Field of Physical Therapy
Bibliographic record
Abstract
Abstract Critical analysis (or the ability to recognize taken-for-granted assumptions and their effects) is a skill that requires teaching and practice. The purpose of this article is to introduce a framework for critically analyzing assumptions within physical therapy and illustrate its utility through application to 2 examples: a physical therapy clinic logo and an outcome measure for health-related quality of life (HRQOL). This 7-step framework for critical analysis was created for a pilot project to develop reflexivity among senior physical therapist students and further developed through an iterative process of reflecting on its utility for advancing the field of physical therapy. The 7-step framework is an iterative process involving a cascade of 7 steps: (1) name the specific aspect of practice being analyzed, (2) identify the intended purposes of this aspect of practice, (3) uncover the assumptions that support these intended purposes, (4) identify who benefits, (5) identify who is disadvantaged, (6) link these specific ideas to society-level patterns, and (7) conceive of alternatives that mitigate actual or potential harms. It is emphasized that being theoretically critical does not equate to being negative. Rather, the word “critical” is used in the sense of thinking deeply and carefully about the intended and unintended consequences of actions (including common professional practices, ways of speaking, and visual representations) in order to reflect on and mature the field of physical therapy. The purpose of critical analysis is to invite and promote dialogue that assists physical therapist clinicians, researchers, and students to arrive at new insights about the impacts of their day-to-day actions.
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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 | 0.213 | 0.120 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.015 | 0.008 |
| Science and technology studies | 0.016 | 0.067 |
| Scholarly communication | 0.018 | 0.017 |
| Open science | 0.007 | 0.014 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".