Knowledge, Attitudes, and Practice of In-Patient Physiotherapists in Ontario Regarding Patients Who Are Super-Morbidly Obese
Bibliographic record
Abstract
Purpose: This article describes the knowledge, attitudes, and practice of in-patient physiotherapists in Ontario regarding the treatment of patients who are super-morbidly obese (SMO; i.e., those whose BMI is >50). Method: A 62-item questionnaire was developed to assess demographics, sources of knowledge, current practice, and attitudes such as confidence, willingness, and the perceived effectiveness of treatment. It was distributed electronically using FluidSurveys. All physiotherapists working in a clinical role with adults in an in-patient setting in Ontario were eligible to participate. Results: A total of 276 physiotherapists completed the survey. Most of them had learned about the treatment from non-structured sources such as clinical experience. More than half (52%) of the participants disagreed that their place of employment was well prepared to facilitate the treatment of patients who are SMO. The majority of respondents were confident in treating these patients, were willing to treat them (82%), and believed that physiotherapy would improve at least one health outcome (96%) for them; however, 46% were reluctant to treat for fear of personal injury. Participants most commonly felt limited by lack of equipment and lack of staff to assist. Conclusions: Physiotherapists have positive attitudes toward treating patients who are SMO, and increased equipment and staff to assist, as well as appropriate education, may decrease the fear of injury for physiotherapists while treating these patients and improve health outcomes for them.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".