Revealing and acting on patient care experiences: exploring the use of Photovoice in practice development work through case study methodology
Bibliographic record
Abstract
Traditional efforts in healthcare to evaluate patient satisfaction with care, an outcome expected from effective person-centered practices, rely heavily on survey methodology. At West Park Healthcare Centre, a rehabilitation and complex continuing care facility in Ontario, Canada, data from patient satisfaction surveys were proving insufficient on their own to inform and ultimately motivate those in a position to bring about improvement in person-centered practices. Additional and more effective strategies were therefore sought as part of a larger practice development initiative to evaluate our progress in accomplishing person-centered outcomes for our patients and to guide the planning of continuous improvement strategies. \nPhotovoice was selected and evaluated through qualitative within-site, multiple case study design as a method to reveal the care experiences of patients living in complex continuing care and facilitate change based on expressed needs and concerns. The findings suggest Photovoice improves understanding of: \n\tThe factors that influence patient satisfaction with care \n\tThe practice changes required to enhance the person-centeredness of that care \n \nHowever, Photovoice did not result in consistent shifts in care practices based on the improved understanding. Going forward, these findings suggest more work is needed to help the organisation move beyond an understanding of what matters to patients to acting on that knowing. \n
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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 | 0.052 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.015 | 0.023 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.004 | 0.004 |
| 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".