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Record W2323584913

Patients' narratives of open-heart surgery : emplotting the technological.

2009· dissertation· en· W2323584913 on OpenAlexvenueno aff
Jennifer Lapum

Bibliographic record

VenueLibrary and Archives Canada (Government of Canada) · 2009
Typedissertation
Languageen
FieldMedicine
TopicEmpathy and Medical Education
Canadian institutionsnot available
Fundersnot available
KeywordsNarrativeMedicineArtLiterature
DOInot available

Abstract

The steady increase of technology has become particularly ubiquitous in environments of heart surgery. Patients in these environments come into close contact with technology in its many guises. Often, practitioners may be deterred from engaging with patients because technology and the associated routines of care become the focus. As a result, it is important to understand how patients make sense of the technological situations encountered during treatment and recovery with attention to the constitution of identity and emerging moral issues. A narrative methodology was employed to examine patients’ experiential accounts of the technological in open-heart surgery and recovery. Sixteen patients were interviewed 3-4 days after surgery and 4-6 weeks after discharge, in addition participant journals were employed. \nStudy results pointed to the technological as the dominant discourse in heart surgery and recovery, strongly organizing health care practices and patients’ recovery. These discursive influences shaped participants’ stories resulting in two temporal shifts of authorial voice. Authorial voice reflects the dominant discourse and structured how stories unfolded. The first temporal shift exhibited how technology acted as the authorial voice, structuring stories of the preoperative and early postoperative period. Although participants were the narrators of their own stories, they were strongly influenced by the dominant discourse of the technological and its \nassociated dimensions of care. Participants’ stories revealed how patients were at the centre of activity, but passive, universal and undifferentiated. Although technology continued to influence stories of the later postoperative period and recovery at home, there was a shift of authorial voice to participants. Narratives reflected how the technological was incorporated into participants’ daily lives, but their stories included more personal elements rooted in their own particularities.\nStudy implications involve a critical uptake of technology that emphasizes the balance between technologically- and humanistically-focused practices in heart surgery and recovery. A key implication is the critical need to encompass affective and social dimensions of patients within the technologically-driven practices of heart surgery. Of great significance is how practitioners, particularly nurses, can act as supporting characters in helping with transitions of authorial voice from the technological back to the participant.

Stored with the screening record, where it is evidence for the labels above.

How this classification was reachedexpand

The three-model screen

all 5,600 screened works →

All three models called this out of scope.

stratum: venue_new · design weight: 2684.25 (the sample is stratified; any rate computed without the weight is wrong)
Claude Opus 4.8OUT
genre: empirical
about Canada: no
confidence: medium

Narrative study of patients' experience of technology in heart surgery; studies clinical care, not scientific practice, so it falls outside the STS-of-research sense of T2.

GPT-5.6 (high)OUT
genre: empirical
about Canada: no
confidence: high

The work studies patients' narratives and experiences of open-heart surgery technology.

Grok 4.5OUT
genre: empirical
about Canada: no
confidence: high

Narrative study of patients’ experience of technology in open-heart surgery is clinical/medical sociology, not study of scientific research practice.

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)0.007
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.993
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.021
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0070.012
Scholarly communication0.0080.007
Open science0.0020.008
Research integrity0.0030.006
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 head0.007
GPT teacher head0.202
Teacher spread0.196 · 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.

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

Citations8
Published2009
Admission routes1
Has abstractyes

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