MétaCan
Menu
Back to cohort

Increasingly distant from life: problem setting in the organization of home care

2007· article· en· W1911156104 on OpenAlexaff
Christine Ceci

Bibliographic record

VenueNursing Philosophy · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicHealthcare innovation and challenges
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsRhetorical questionVariety (cybernetics)NarrativeSpace (punctuation)Process (computing)Health careField (mathematics)PsychologySociologyPublic relationsEpistemologyComputer sciencePolitical scienceLinguisticsLawArtificial intelligence

Abstract

fetched live from OpenAlex

The analysis undertaken in this paper explores the significance of a central finding from a recent field study of home care case management practice: a notable feature of case management work is the preparation of an orderly, ordered space where care may be offered. However, out of their encounters with an almost endless variety of situations, out of people's diverse narratives of need, case managers seem able to pick out only limited range of recognized needs to which to respond and demonstrate a series of responses themselves equally limited. Though this observation suggests a kind of efficiency that is currently highly valued within healthcare systems, it also underlines the system's inability to engage difference and variability in a meaningful way. This inability or limitation in effectively engaging difference is conceptualized here as, in some sense, a problem, and the nature of this problem is explored through the rhetorical process of problem setting. The central question becomes how might we develop and deploy an orderly and coherent system of care without essentializing people's experiences, without treating these experiences reductively, without, in a Foucaultian frame of reference, allowing what can be understood as similarity or resemblance among clients and situations to be folded back into sameness? As we encounter complexity, variability and difference in practice, how should we treat it?

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)0.001
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.181
Threshold uncertainty score0.231

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.036
GPT teacher head0.340
Teacher spread0.304 · 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 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

Citations18
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueNursing PhilosophySame topicHealthcare innovation and challengesFrench-language works237,207