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Record W2104685824 · doi:10.1177/0192513x04270208

How the Trivialization of the Demands of High-Tech Care in the Home is Turning Family Members Into Para-Medical Personnel

2005· article· en· W2104685824 on OpenAlexaffabout
Nancy Guberman, Éric Gagnon, Denyse Côté, Claude Gilbert, Nicole Thivièrge, Marielle Tremblay

Bibliographic record

VenueJournal of Family Issues · 2005
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsUniversité du Québec à RimouskiUniversité du QuébecUniversité du Québec en OutaouaisUniversité du Québec à Montréal
Fundersnot available
KeywordsContext (archaeology)Health careNursingAcute careAmbulatory careMedicinePsychologyFamily medicinePolitical science

Abstract

fetched live from OpenAlex

This study analyzes the transfer of specialized professional activities from health care workers to patients and their family members in the context of the shift to ambulatory care for acute and chronic illnesses requiring hospitalization. Based on 119 semidirective interviews with patients released from hospital after early discharge and/or with the family members caring for them, and based on 26 focus groups and 9 individual interviews with health care professionals from hospitals and home care agencies in five regions of the province of Québec, this article raises the issue of the trivialization of professional care which underlies this transfer. This article also examines two stages in this trivialization: the preparation for discharge and the transfer of specialized activities. Theoretical and empirical implications include the need to better understand how health care workers support this transfer through a process of trivialization and the implications of this transfer for patients and their families.

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)0.002
metaresearch head score (Gemma)0.010
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 score0.082
Threshold uncertainty score0.163

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0070.009
Scholarly communication0.0040.003
Open science0.0010.003
Research integrity0.0010.001
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 head0.042
GPT teacher head0.376
Teacher spread0.334 · 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

Citations28
Published2005
Admission routes2
Has abstractyes

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