Bringing wonder into medicine: a film-based curriculum
Bibliographic record
Abstract
The proposed workshop presents a film-based curriculum in whole person care.This curriculum includes one 37-minute film (A Certain Kind of Light – screened in 18 film festivals around the world) and six smaller films that are each 5-10 minutes in length, and are accompanied by a journaling process (all of which explore whole person care in medicine). These are not the traditional “teaching” films, but films developed from a more artistic/creative standpoint, for the purposes of affecting attitudes, as much as teaching skills. The workshop will include the following: Introduction: Discussion of how the film-based curriculum impacts culture change as much as personal change of the healthcare practitioner. Content: If there is not enough time to show all 7 films, we would present at least four of them to help the participant understand both philosophy and content of the films, illustrating how whole person care concepts can be taught through an aesthetic avenue to more immediately affect thoughts, feelings, attitudes, and behaviors. In addition, we will discuss the journaling process, clinical rounds, and research that accompany the film curriculum. Finally, the panel will join together in discussing how this type of mechanism contributes to culture shift within an organization. Those presenting in this workshop are those who developed, teach and conduct research on this curriculum. This curriculum is newly developed (as of 2017) and is being used in a variety of healthcare disciplines within one healthcare educational and clinical entity and has been requested by several others.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.023 | 0.005 |
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".