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Record W2937880007 · doi:10.1177/0840470418823220

Institutional ethnography as a unique tool for improving health systems

2019· article· en· W2937880007 on OpenAlexaff
Emily Rowland, Myuri Manogaran, Ivy Lynn Bourgeault

Bibliographic record

VenueHealthcare Management Forum · 2019
Typearticle
Languageen
FieldMedicine
TopicInfant Development and Preterm Care
Canadian institutionsNatural Resources CanadaRoyal College of Physicians and Surgeons of CanadaOntario Tech UniversityUniversity of OttawaPublic Health OntarioUniversity of Toronto
Fundersnot available
KeywordsEthnographyQualitative researchHealth carePublic relationsSociologyPerceptionEveryday lifeHealthcare systemPsychologyPolitical scienceSocial science

Abstract

fetched live from OpenAlex

Qualitative research in the health system has made tremendous developments in the last decade to better understand patient experiences. What is often overlooked, are the influences that the internal structures, policies and people have on the individuals that use health services. Institutional ethnography is a qualitative approach that aims to capture the social organization of "everyday life" at various system levels. An institutional ethnographic framework was applied to two research studies exploring how families experience care in neonatal intensive care units. Data were collected to develop a deep understanding of the social contexts that exist within institutional boundaries. This paper provides evidence that how care is organized and delivered can significantly influence patient experiences, perceptions and ultimately health outcomes. Adopting institutional ethnographic techniques as a common research method is a valuable tool for health leaders seeking to understand and develop recommendations for health system reform.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.949
Threshold uncertainty score0.811

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 integrity0.0000.000
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 head0.019
GPT teacher head0.303
Teacher spread0.284 · 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 designNot applicable
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

Citations14
Published2019
Admission routes1
Has abstractyes

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