MétaCan
Menu
Back to cohort
Record W2891368640 · doi:10.1108/qaoa-11-2017-0049

How could discourse theories of identity formation critically engage patient-centered care in older adults?

2018· article· en· W2891368640 on OpenAlexaff
Anna Horton, Simon Horton

Bibliographic record

VenueQuality in Ageing and Older Adults · 2018
Typearticle
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsMcGill University
Fundersnot available
KeywordsOperationalizationIdentity (music)EmpowermentContext (archaeology)SociologyOriginalityIdentity formationPsychologySocial psychologyPublic relationsEpistemologySelf-conceptPolitical scienceAestheticsLaw

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to explore how discourse theories can contribute to the concept of identity formation within a patient- or person-centered care (PCC) orientation, to enable more critical engagement with PCC in older people. Design/methodology/approach This is a conceptual paper. Findings This paper concludes that the discourse literature has important insights for understanding identity formation in older people as operationalized in the context of PCC in three particular ways: accounting for multiplicity in patients’ identity; exploring “the devolution of responsibility” to address shifts in performing identities in clinical encounters; and attending to a “crisis of positioning” to engage empowerment discourse within a PCC philosophy. Originality/value Whilst a notion of patient identity is at the heart of PCC, the concept remains inconsistent and underdeveloped. This is particularly problematic for the quality of care in older adults, as PCC has become increasingly synonymous with care of older people. Discourse theories of identity formation can be used to critically engage with identity within the context of PCC, so as to develop more nuanced understandings of “the person” or “the patient,” with the potential to improve research into care for aging and older adults.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.156
Threshold uncertainty score0.974

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.080
GPT teacher head0.426
Teacher spread0.346 · 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

Citations3
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueQuality in Ageing and Older AdultsSame topicPatient-Provider Communication in HealthcareFrench-language works237,207