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Record W1553097724 · doi:10.1300/j010v41n02_02

Struggles Between the Body and Machine

2005· article· en· W1553097724 on OpenAlexaff
Stephen Giles

Bibliographic record

VenueSocial Work in Health Care · 2005
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsToronto General Hospital
Fundersnot available
KeywordsInterpretative phenomenological analysisDehumanizationTheme (computing)DilemmaPsychologySocial psychologyParticipant observationSocial workSubversionPsychological interventionQualitative researchSociologyPoliticsPsychiatrySocial scienceEpistemologyComputer science

Abstract

fetched live from OpenAlex

This study explored the life-world of individuals being treated for end stage renal disease with a home haemodialysis machine (HHDM). A phenomenological framework was employed to gain an understanding of the lived-body in relation to the HHDM in order to assist in the planning of future social work interventions. A purposive sample of three participants who had each experienced various lengths of HHDT, were interviewed using two semi-structured in-depth interviews, one week apart. Interviews were conducted at a place of the participant's choice. Participants were asked to speak about their experience of HHDT and how it related to their bodily experience. The findings were interpreted using interpretive phenomenological analytic methods. The central theme that arose from the data was "Struggles between the body and machine". This theme spoke to paradoxical dilemma of living with a life saving machine that you have no control over. Implications suggest that the machine's tendency to be personified, means that it be considered as family member in social assessment. Emotional support to patients should acknowledge the machines dehumanizing tendencies and as well as the issue of its personification. Finally, a patients unmet needs may turn to subversion if not addressed by staff, suggesting that a systematic process to empower patients be established.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.248
Threshold uncertainty score0.227

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.099
GPT teacher head0.444
Teacher spread0.345 · 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 designObservational
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

Citations7
Published2005
Admission routes1
Has abstractyes

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