To Know Their Stories: Using Playbuilding to Develop a Training/Orientation Video on Person-Centered Care
Bibliographic record
Abstract
This study explores the experiences of health care staff and family members who provide support for people living with dementia and traumatic brain injury. Using a playbuilding methodology (Belliveau, 2006; Norris, 2009; Perry, Wessels & Wager, 2013) in which theatre performers devised short vignettes based on focus group interviews with health care providers, an educational video was produced. The video will be shown to the focus group interviewees in order to generate further conversation—knowledge co-creation—on the supportive and resistive practices in person-centred care (Leplege, Gzil, Cammelli, Lefeve, Pachoud & Ville, 2007; Kadri, Rapaport, Livingston, Cooper, Robertson & Higgs, 2018; Santana, Manalili, Jolley, Zelinsky, Quan & Lu, 2018), a philosophical approach that privileges the holistic needs of the individual rather than the bio-medical and administrative urgencies of the medical system. I outline the process of developing vignettes, videoing them and editing the video using a constructivist approach and an application of narrative and film theory. This work adds to the discussion of how the health care system may benefit from arts-based methods of knowledge construction.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".