MétaCan
Menu
Back to cohort
Record W2883793230 · doi:10.5737/23688076283166171

Patient-reported care domains that enhance the experience of “being known” in an ambulatory cancer care centre

2018· article· en· W2883793230 on OpenAlexaffvenueabout
Chloe Grover, Erin Mackasey, Erin Cook, Lucie Tremblay, Carmen G. Loiselle

Bibliographic record

VenueCanadian Oncology Nursing Journal · 2018
Typearticle
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-MontréalMcGill UniversityJewish General Hospital
Fundersnot available
KeywordsFeelingPerceptionQualitative researchNursingAffect (linguistics)AmbulatoryPsychologyAmbulatory careMedicineHealth careSocial psychologySociology

Abstract

fetched live from OpenAlex

PURPOSE: This study explored patients' perceptions of "being known" in an ambulatory chemotherapy unit. METHODS: Using a qualitative descriptive design, 10 participants with various cancer diagnoses were recruited from a large cancer centre in Montreal, Quebec. Audiotaped individual interviews were transcribed verbatim. Textual data were coded and analyzed thematically. FINDINGS: Participants spoke of their need to have the staff approach them as individuals first and then as persons with cancer. They further underscored the importance of: (1) feeling truly welcome in the cancer care environment, (2) being provided with person- and situation-responsive care, and (3) considering occupational and social roles that go beyond the "sick role". Mutual patient-nurse disclosure also contributed to perceptions of a personalized care approach. IMPLICATIONS FOR NURSING: In addition to key elements construed as crucial for enhancing perceptions of being known, future studies should further document how the interplay among demographic, physical/psychological, and cultural factors affect these perceptions.

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 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 score0.618
Threshold uncertainty score0.999

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 studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.109
GPT teacher head0.451
Teacher spread0.342 · 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 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

Citations15
Published2018
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Oncology Nursing JournalSame topicPatient-Provider Communication in HealthcareFrench-language works237,207