Enhancing the healthcare quality improvement storyboard using photovoice
Bibliographic record
Abstract
Visual photographic approaches have steadily gained momentum in health services research in the last 20 years; however, its use in quality improvement (QI) is sparse.1 Currently, the traditional A3 QI story board is an integral component for sharing health service improvement efforts. Named after the A3 international paper size of approximately 11″ × 17″, this one page/poster provides a visually concise synopsis of the problem, the root causes identified, the resolution and metrics that indicate resolution effect.2 The benefit of the A3 QI storyboard approach is in the thinking and behaviours it stimulates along with facilitating dialogue.3 Photovoice is a visual participatory method in which photographic images are taken to strengthen and supplement the more robust metrics involved in QI.4 The photographic image triangulates with the conventionally generated quantitative and qualitative findings to create a more comprehensive QI story.5 6 In QI studies that have used photographic images, the rationale for inclusion are, first, to alleviate challenges related to change acceptance as healthcare employees may gain a better understanding of why the improvement is a priority. Second, to facilitate collaboration between different stakeholder groups, and lastly the photographs may lead to a more direct understanding of people, their life experiences and perceptions enabling others to empathise and understand the QI effort.7 Within the setting of healthcare, engaging providers and patients, obtaining their buy-in and understanding their unique perspectives/experience are essential to improve the quality of care.8–10 Here, we describe an initiative that undertook …
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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 | 0.023 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.027 | 0.004 |
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".