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Record W3144446614 · doi:10.1108/jhom-07-2020-0292

Metaphors of organizations in patient involvement programs: connections and contradictions

2021· article· en· W3144446614 on OpenAlexaffabout
Paula Rowland, Carol Fancott, Julia Abelson

Bibliographic record

VenueJournal of Health Organization and Management · 2021
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsImpactCanadian Foundation for Healthcare ImprovementThe Wilson CentreMcMaster UniversityUniversity of Toronto
Fundersnot available
KeywordsOrganizational learningMetaphorFraming (construction)SociologyOrganizational cultureNarrativeOrganization developmentUnderpinningOrganizational theoryPublic relationsPsychologyEpistemologyKnowledge managementManagementPolitical scienceComputer science

Abstract

fetched live from OpenAlex

PURPOSE: = 20) from participants in patient engagement activities from two case study organizations in Ontario, Canada. Inspired by classic organizational scholars, we ask "what is the organization that it might learn from patients?" DESIGN/METHODOLOGY/APPROACH: Patient involvement activities are used as part of quality improvement efforts in healthcare organizations worldwide. One fundamental assumption underpinning this activity is the notion that organizations must "learn from patients" in order to enact positive organizational change. Despite this emphasis on learning, there is a paucity of research that theorizes learning or connects concepts of learning to organizational change within the domain of patient involvement. FINDINGS: Through our analysis, we interpret a range of metaphors of the organization, including organizations as (1) power and politics, (2) systems and (3) narratives. Through these metaphors, we display a range of possibilities for interpreting how organizations might learn from patients and associated implications for organizational change. ORIGINALITY/VALUE: This analysis has implications for how the framing of the organization matters for concepts of learning in patient engagement activities and how misalignments might stymie engagement efforts. We argue that the concept and commitment to "learning from patients" would be enriched by further engagement with the sociology of knowledge and critical concepts from theories of organizational learning.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.381
Threshold uncertainty score0.435

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.074
GPT teacher head0.354
Teacher spread0.280 · 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

Citations11
Published2021
Admission routes2
Has abstractyes

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