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Record W2800440493 · doi:10.1177/1049732318768239

Developing a Compassionate Culture Within Pediatric Rehabilitation: Does the Schwartz Rounds™ Support Both Clinical and Nonclinical Hospital Workers in Managing Their Work Experiences?

2018· article· en· W2800440493 on OpenAlexafffund
Keith Adamson, Sonia Sengsavang, Sakeena Myers-Halbig, Nancy Searl

Bibliographic record

VenueQualitative Health Research · 2018
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsWilfrid Laurier UniversityHolland Bloorview Kids Rehabilitation HospitalUniversity of Toronto
FundersBloorview Research Institute
KeywordsThematic analysisRehabilitationContext (archaeology)NursingMedicinePsychologyHealth careQualitative researchPhysical therapy

Abstract

fetched live from OpenAlex

Schwartz Rounds™ offers an interprofessional forum for staff to openly engage in discussions about social-emotional aspects of care. We aimed to assess the perceived impact of Rounds in the health care context of pediatric rehabilitation, as well as a comparative analysis of how Rounds affected clinical versus nonclinical staff. Does effect on perceived outcomes was also investigated. Data were collected from 29 hospital staff (15 clinicians, 14 nonclinicians) who attended one, two, or three+ Rounds via semistructured interviews. Thematic analysis indicated impacts at the personal and social levels (e.g., reduced stress, increased level of approaching behaviors, normalizing and validating emotional experiences, and building bridges within the hospital). Data also revealed the novel finding of Rounds affecting professional knowledge and skills (e.g., interprofessional practice, reflective practice, clinical imagination). These findings elucidate how Schwartz Rounds™ is beneficial in a pediatric rehabilitation setting, albeit somewhat differentially for clinical and nonclinical staff.

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)0.035
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.159
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0350.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0030.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.003
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 head0.326
GPT teacher head0.655
Teacher spread0.329 · 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

Citations20
Published2018
Admission routes2
Has abstractyes

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