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Record W2104617967 · doi:10.3109/13561820.2012.717553

Meanings and perceptions of patient-centeredness in social work, nursing and medicine: A comparative study

2012· article· en· W2104617967 on OpenAlexaff
David Gachoud, Mathieu Albert, Ayelet Kuper, Lynfa Stroud, Scott Reeves

Bibliographic record

VenueJournal of Interprofessional Care · 2012
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMeaning (existential)Context (archaeology)Social workHierarchyNursingPsychologyPerceptionMedicineMedical educationPsychotherapist

Abstract

fetched live from OpenAlex

Answering patients' evolving, more complex needs has been recognized as a main incentive for the development of interprofessional care. Thus, it is not surprising that patient-centered practice (PCP) has been adopted as a major outcome for interprofessional education. Nevertheless, little research has focused on how PCP is perceived across the professions. This study aimed to address this issue by adopting a phenomenological approach and interviewing three groups of professionals: social workers (n = 10), nurses (n = 10) and physicians (n = 8). All the participants worked in the same department (the General Internal Medicine department of a university affiliated hospital). Although the participants agreed on a core meaning of PCP as identifying, understanding and answering patients' needs, they used many dimensions to define PCP. Overall, the participants expressed value for PCP as a philosophy of care, but there was the sense of a hierarchy of patient-centeredness across the professions, in which both social work and nursing regarded themselves as more patient-centered than others. On their side, physicians seemed inclined to accept their lower position in this hierarchy. Gieryn's concept of boundary work is employed to help illuminate the nature of PCP within an interprofessional context.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.104
Threshold uncertainty score0.439

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.049
GPT teacher head0.485
Teacher spread0.436 · 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 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

Citations59
Published2012
Admission routes1
Has abstractyes

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