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Record W7036863520

Costly bodies: an examination of long-term care and COVID-19 using autoethnography and critical discourse analysis

2022· dissertation· en· W7036863520 on OpenAlexaboutno aff

Bibliographic record

VenueMspace (University of Manitoba) · 2022
Typedissertation
Languageen
FieldSocial Sciences
TopicHealthcare innovation and challenges
Canadian institutionsnot available
Fundersnot available
KeywordsAutoethnographyInstitutionalisationPerspective (graphical)Health careDiscourse analysisCritical discourse analysisGovernment (linguistics)SituatedCare perspectiveHarm
DOInot available

Abstract

fetched live from OpenAlex

The COVID-19 pandemic intensifies the challenging realities of institutionalization for many disabled people living in care facilities. This thesis project examines the impact of the COVID-19 pandemic on the lives of disabled people living in institutions from the perspective of a care provider. This project is situated within the theoretical frameworks of the political/relational model of disability, along with Crip theory. Data was collected from personal journal excerpts, as well as from health care protocols released from the Government of Saskatchewan and governing health care bodies. Data analysis through analytic autoethnography and critical discourse analysis revealed themes including isolation and societal understandings of the disposability of disabled individuals. This thesis project looks to communicate the realities of long-term care and highlight the harm health care protocols had for disabled individuals during the COVID-19, from the perspective of a care worker, while challenging ideas of institutionalization and merging thoughts around aging and disability studies.

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)0.023
metaresearch head score (Gemma)0.025
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 score0.023
Threshold uncertainty score0.123

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.025
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.005
Science and technology studies0.0200.034
Scholarly communication0.0130.010
Open science0.0020.012
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0030.000

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 head0.066
GPT teacher head0.384
Teacher spread0.318 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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