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Record W4412932683 · doi:10.1080/09687599.2025.2536587

(Re)humanizing clinical documentation for disabled children: a cascade of potential outcomes of critically reflective practice

2025· article· en· W4412932683 on OpenAlexaffabout
Victoria Boyd, Nicole N. Woods, Wenonah Campbell, Arno K. Kumagai, Stella Ng

Bibliographic record

VenueDisability & Society · 2025
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsCentre for Disability Prevention and RehabilitationUniversity Health NetworkMcMaster UniversityUniversity of Toronto
FundersSpencer Foundation
KeywordsDocumentationCritically illPsychologyCascadeClinical PracticeMedical educationMedicineNursingComputer scienceIntensive care medicineEngineering

Abstract

fetched live from OpenAlex

Supporting disabled children at school requires collaboration between health professionals, educators, and families. As a primary mode of communication, clinical letters support or challenge collaboration. Critically reflective practice prompts health professionals to reimagine how clinical letters are written and used. This critical qualitative study examines the impacts of a critically reflective approach to letter writing from the perspectives of parents and educators. Nine parents and eight educators in Ontario, Canada participated in semi-structured elicitation interviews. Our findings reveal a cascade of potential humanistic, communication, collaboration, and advocacy outcomes. Our study highlights the role of critically reflective practice in supporting a humanistic approach to clinical documentation and challenges the prevailing belief that a deficit-oriented approach is the most effective way to advocate. Achieving the outcomes identified in our study relies on fundamental shifts in how health professionals conceptualize documentation, and must be paired with broader systems change.

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)0.002
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.085
Threshold uncertainty score0.723

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.044
GPT teacher head0.547
Teacher spread0.502 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations1
Published2025
Admission routes2
Has abstractyes

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