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Record W2987285097 · doi:10.35680/2372-0247.1386

Patient perspectives: Four pillars of professionalism

2019· article· en· W2987285097 on OpenAlexaff
Laura Yvonne Bulk, Donna Drynan, Sue Murphy, Patricia Gerber, Roberta Bezati, Sacha Trivett, Tal Jarus

Bibliographic record

VenuePatient Experience Journal · 2019
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsEmpathyCompetence (human resources)Health careFocus groupGrounded theoryPsychologyPerspective (graphical)Medical educationInterpretative phenomenological analysisCore competencyQualitative researchNursingMedicineSocial psychologySociology

Abstract

fetched live from OpenAlex

Professionalism is a core component of healthcare practice and education; however, there is often not a consistent description of professionalism, and current definitions lack a key perspective: that of the patient. This study aimed to deepen understandings of patients’ perspectives on how professionalism should be enacted by healthcare providers. Using a phenomenological approach informed by constructivist theory, the study team conducted semi-structured interviews and focus groups with 21 patients to ascertain their views on professionalism. Data analysis was conducted using a constant comparative approach wherein initial analysis informed subsequent data collection. Participant themes fell into four pillars of professionalism: taking a collaborative human-first approach; communicating with heart and mind; behaving with integrity; and practicing competently. This study highlights patient perspectives on professionalism and examines consistencies and differences between those perspectives and those of healthcare providers, which are extensively described in the literature. While published literature highlights competence and communication as main aspects of professionalism which our participants also focused on, participants in this study emphasized integrating patients into care teams, employing empathy, and demonstrating integrity. Experience Framework This article is associated with the Patient, Family & Community Engagement lens of The Beryl Institute Experience Framework. (http://bit.ly/ExperienceFramework) Access other PXJ articles related to this lens. Access other resources related to this lens.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.217
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0020.000

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 head0.024
GPT teacher head0.347
Teacher spread0.323 · 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

Citations10
Published2019
Admission routes1
Has abstractyes

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