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Record W7008289478

Beyond Professional Affiliation: Race, Class & Gender Dynamics in Interdisciplinary Teams

2014· dissertation· en· W7008289478 on OpenAlexaboutno aff

Bibliographic record

VenueMacSphere (McMaster University) · 2014
Typedissertation
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsnot available
Fundersnot available
KeywordsNucleofectionGestational periodPretextCircumstantial evidenceTSG101Diafiltration
DOInot available

Abstract

fetched live from OpenAlex

<p>This study aims to illuminate the ways that gender, race, and class are experienced and socially constructed on interdisciplinary health teams. The study involves four in-depth qualitative interviews with social workers who are employed members of interdisciplinary health teams within a medium sized city in south-western Ontario. The study documents three levels of inquiry. Initially, it explores social workers' understandings of how gender, race, and class affect interdisciplinary team dynamics. Next, a discourse analysis ofthe interviewees' accounts reveals how some conceptualizations of gender, race, and class are potentially limiting and at times reinforces the status quo. Lastly, it traces invisible relations of domination and subordination conveyed through the social organization of knowledge around interdisciplinary teams.</p> <p>The study offers insight into the ways that interdisciplinary health teams are thought to both promote and undermine cultural competency initiatives. It also reveals how gender, race, and class issues on interdisciplinary teams are conceptualized in ways that preserve the status quo. However, the study challenges the notion that education and exposure to difference and diversity alone will foster cultural competency skills. The study concludes that both cognitive and material shifts in power are necessary in order to achieve an effective redistribution ofpower within interdisciplinary teams.</p>

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.274
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.1240.002

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 head0.016
GPT teacher head0.343
Teacher spread0.327 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2014
Admission routes1
Has abstractyes

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