Walking the Talk: “Reflexivity” to Advance Integration of Patient Reported Outcomes for Cancer Care Screening
Bibliographic record
Abstract
In this commentary, we propose the use of video-reflexive ethnography (VRE) as a means to support integration of patient-reported outcomes (PROs) in cancer care screening. As for any policy or intervention, the optimization of PROs depends on moving beyond their mere formal introduction, and depends on the integration of PROs in the everyday practice contexts of health care professionals (HPEs). The use of VRE allows for video-playback sessions among oncology professionals to support team-based learning and practice-change grounded in "reflexivity." Through a review of previous methods used to support organizational change in healthcare settings (e.g., policies, quality improvement initiatives, simulation sessions), we present some unsung advantages of VRE that can be applied to a complex integrated setting, such as cancer care. As opposed to other methods to create change, VRE does not dictate new measures, but rather supports "bottom-up" provider-initiated changes to health care practices and contexts, grounded in collaborative day-to-day practice. We argue that VRE optimizes PROs in cancer care by facilitating their effective and sustainable integration, to promote improved patient care.
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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 | 0.071 | 0.223 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.012 | 0.023 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".