Tinkering with responsive caring in disabled children's healthcare: Implications for training and practice
Bibliographic record
Abstract
Health professional education has traditionally relied on the acquisition of vertical expertise whereby learners apply top-down principles and methods to develop clinical skills. In this critical qualitative study, we examined horizontal processes of “knotworking” and “tinkering” in the development of expertise amongst clinicians and trainees in a children's rehabilitation outpatient clinic. Using ethnographic methods of observation, interviews and group dialogues, the study explored what constitutes “good” child healthcare, the risks of separating humanistic from biomedical care, and how discursive assumptions and conventions shaped learning and practices. Our analyses identified processes of responsive caring that integrated medical and humanistic imperatives into transposable, dynamic repertoires through which clinicians could pivot in response to child and family needs and priorities, resource access, and socio-material contexts. We discuss the challenges for teaching and mentoring medical trainees who have to both learn and unlearn particular practices in their efforts to develop responsive expertise.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.027 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".