Using photovoice to identify patient transfers risk factors in long-term care home settings
Bibliographic record
Abstract
The variety of tasks performed in a variety of organisations complicates having a gold standard tool for identifying workplace injury risk factors. There are three main approaches for identifying risk factors; self-reports (e.g., surveys, focus groups, interviews), direct observation (e.g., checklists), and direct measurement (e.g., electromyography). Alternatively, this study aimed to use photovoice, a participatory approach that uses photographs to evoke discussion and the identification of issues, or in this case risk factors. The purpose of this study was to determine whether photovoice strategies could be useful to help workers identify risk factors inherent in lifting and transferring residents in nursing homes. It was found that photovoice is a viable method for identifying risk factors. Overall, the photovoice method was enjoyed by the participants, was advantageous in identifying risk factors, and promoted group participation, communication, and empowerment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".