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Record W2516818571 · doi:10.1111/medu.13050

Blurring the boundaries: using institutional ethnography to inquire into health professions education and practice

2016· article· en· W2516818571 on OpenAlexaff
Stella Ng, Laura Bisaillon, Fiona Webster

Bibliographic record

VenueMedical Education · 2016
Typearticle
Languageen
FieldNursing
TopicNursing education and management
Canadian institutionsThe Scarborough HospitalThe Wilson CentreWomen's College HospitalCentre for Global Health ResearchUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsEthnographySociologyQualitative researchContext (archaeology)Health carePopularityEngineering ethicsEpistemologyPsychologySocial scienceSocial psychologyPolitical science

Abstract

fetched live from OpenAlex

CONTEXT: Qualitative, social science approaches to research have surged in popularity within health professions education (HPE) over the past decade. Institutional ethnography (IE) offers the field another sociological approach to inquiry. Although widely used in nursing and health care research, IE remains relatively uncommon in the HPE research community. This article provides a brief introduction to IE and suggests why HPE researchers may wish to consider it for future studies. METHODS: Part 1 of this paper presents IE's conceptual grounding in: (i) the entry point to inquiry ('materiality'), (ii) a generous definition of 'work' and (iii) a focus on how 'texts' such as policies, forms and written protocols influence activity. Part 2 of this paper outlines the method's key features through exemplars from our own research. Part 3 discusses the ways in which research that blurs the lines between educational and clinical practice can be both generative for HPE and accomplished using IE. RESULTS: The authors demonstrate the usefulness of IE for studying complex social issues in HPE. It is posited that a key added value of IE is that it goes beyond individual-level explanations of problems and phenomena, yet also closely studies individuals' activities, rather than remaining at an abstract or distant level of analysis. Thereby, IE can result in feasible and meaningful social change at the nexus of health professions education and other social systems such as clinical practice. CONCLUSIONS: IE adds to the growing qualitative research toolkit for HPE researchers. It is worth considering because it may enable change through the study of HPE in relation to other social processes, structures and systems, including the clinical practice world. A particular benefit may be found in blending HPE research with research on clinical practice, toward changing practice and policy through IE, given the interrelated nature of these fields.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.063
metaresearch head score (Gemma)0.087
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.063
Threshold uncertainty score0.332

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0630.087
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.004
Science and technology studies0.0110.033
Scholarly communication0.0120.018
Open science0.0030.017
Research integrity0.0030.004
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 head0.038
GPT teacher head0.437
Teacher spread0.399 · 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 source (direct Gemma or distilled Codex), 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

Citations35
Published2016
Admission routes1
Has abstractyes

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