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Record W4377292373 · doi:10.1016/j.ssmqr.2023.100286

Tinkering with responsive caring in disabled children's healthcare: Implications for training and practice

2023· article· en· W4377292373 on OpenAlexaff
Barbara E. Gibson, Yani Hamdani, Bhavnita Mistry, Anne Kawamura

Bibliographic record

VenueSSM - Qualitative Research in Health · 2023
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsHolland Bloorview Kids Rehabilitation HospitalUniversity of Toronto
Fundersnot available
KeywordsHealth careMedical educationHumanismEthnographyPsychologyQualitative researchResource (disambiguation)NursingPedagogyMedicineSociologyComputer science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.027
metaresearch head score (Gemma)0.026
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.430
Threshold uncertainty score0.982

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0270.026
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.484
GPT teacher head0.669
Teacher spread0.185 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2023
Admission routes1
Has abstractyes

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