MétaCan
Menu
Back to cohort
Record W813295813 · doi:10.1177/1049732315591484

Feeling Well and Having Good Numbers

2015· article· en· W813295813 on OpenAlexafffund
Helen H. Kang, Terese Stenfors

Bibliographic record

VenueQualitative Health Research · 2015
Typearticle
Languageen
FieldHealth Professions
TopicEthics in medical practice
Canadian institutionsAIDS VancouverUniversity of British Columbia
FundersSFU Community Trust Endowment FundSimon Fraser University
KeywordsFeelingActive listeningMedicineHealth professionalsAffect (linguistics)NarrativeDialysisNursingKidney diseasePsychologyHealth careSocial psychologyPsychiatryPsychotherapistInternal medicine

Abstract

fetched live from OpenAlex

Individuals living with chronic kidney disease (CKD) must be mindful of their diet and exercise, take multiple medications, and deal with other compounding illnesses. We observed renal patients' encounters with health professionals at a renal clinic for tensions and gaps in patients' and health professionals' understandings of "living well" with CKD. We found that the renal patients at the clinic become emotionally invested in the fluctuations in the numbers on their blood work. Narrative practices of health professionals greatly affect how patients emotionally deal with the possibility of dialysis, transplant, death, or aging. Expectations to "live well" can become a moral burden to be a "good" patient. The gaps between the priorities of patients, their caregivers, and health professionals complicate the notion of "living well" with CKD. Trust, rapport and the practice of listening appear to have the greatest impact in addressing these gaps.

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)0.006
metaresearch head score (Gemma)0.016
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 score0.006
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0060.019
Scholarly communication0.0050.005
Open science0.0010.006
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0060.001

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 head0.834
GPT teacher head0.768
Teacher spread0.066 · 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

Citations22
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueQualitative Health ResearchSame topicEthics in medical practiceFrench-language works237,207