Cheer* in Health Care Practice: What It Excludes and Why It Matters
Bibliographic record
Abstract
Clinicians' positive demeanor and "strengths based" focus can include working to create a cheerful atmosphere in health care environments, cheering for improvements in assessment outcomes, and cheering up clients in situations of decline. Drawing from philosopher Karen Barad's theories of inclusions and exclusions, we investigated what comes to matter (and what is excluded from mattering) when there is cheerfulness, cheering, and so forth (cheer*) in the day-to-day practices of a neuromuscular clinic. We worked collaboratively with clinicians, young people with Duchenne muscular dystrophy, and their families to co-examine the clinic in three iterative exploratory method spaces: (a) group "dialogues" with clinicians; (b) consultative interviews with children, families, and clinicians; and (c) transdisciplinary research team analysis sessions. Cheer* made some things matter in the clinic ("normal" physical function, "positive" emotions, test scores, compliance); and excluded others (grief and loss, "non-normative" bodies and lives, alternative practices, embodied knowledge). We discuss implications across health care settings.
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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 | 0.060 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.068 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 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".