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Record W2121188479 · doi:10.1136/ebn.6.3.96

Patients and nurses negotiated home care interactions within 6 interpersonal contexts

2003· letter· en· W2121188479 on OpenAlexaff
Chantal Caron

Bibliographic record

VenueEvidence-Based Nursing · 2003
Typeletter
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsMedicineInterpersonal communicationNursingPsychologyFamily medicineCommunication

Abstract

fetched live from OpenAlex

Spiers JA. The interpersonal contexts of negotiating care in home care nurse-patient interactions. Qual Health Res2002 ; 12 : 1033 –57 [OpenUrl][1][CrossRef][2][PubMed][3][Web of Science][4] QUESTIONS: In home care nurse-patient interactions, what are the interpersonal contexts and social acts through which negotiation occurs? What are the outcomes of unsuccessful and successful negotiation? Qualitative ethology for video based research. A large metropolitan home healthcare agency in the western US. 10 nurse-patient dyads (3 home care nurses and 8 patients; 2 patients were each paired with 2 nurses). Patients were 25–86 years of age and required home care for acute and chronic conditions. Exclusion criterion was inability to communicate verbally because of cognitive or physical impairment. {The 3 home care nurses, who were case managers and had ≥6 years of home care experience, were peer nominated as expert practitioners.}* 31 routine home care visits were videotaped (19 hours of videotape). Nurses and patients participated in separate semistructured interviews before and after the videotaped sessions. The unit of analysis was … [1]: {openurl}?query=rft.jtitle%253DQualitative%2BHealth%2BResearch%26rft.stitle%253DQual%2BHealth%2BRes%26rft.aulast%253DSpiers%26rft.auinit1%253DJ.%2BA.%26rft.volume%253D12%26rft.issue%253D8%26rft.spage%253D1033%26rft.epage%253D1057%26rft.atitle%253DThe%2BInterpersonal%2BContexts%2Bof%2BNegotiating%2BCare%2Bin%2BHome%2BCare%2BNurse-Patient%2BInteractions%26rft_id%253Dinfo%253Adoi%252F10.1177%252F104973202129120430%26rft_id%253Dinfo%253Apmid%252F12365587%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [2]: /lookup/external-ref?access_num=10.1177/104973202129120430&link_type=DOI [3]: /lookup/external-ref?access_num=12365587&link_type=MED&atom=%2Febnurs%2F6%2F3%2F96.1.atom [4]: /lookup/external-ref?access_num=000178120200002&link_type=ISI

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.366
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.007
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 head0.165
GPT teacher head0.426
Teacher spread0.261 · 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.

Study designNot applicable
Domainnot available
GenreCommentary

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

Citations0
Published2003
Admission routes1
Has abstractyes

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