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Record W3016646678 · doi:10.5737/2368807630293102

Taking control over our health: Empowerment as perceived by young adults living with advanced cancer

2020· article· en· W3016646678 on OpenAlexaffvenueabout
Rosalind Garland, Saima Ahmed, Carmen G. Loiselle

Bibliographic record

VenueCanadian Oncology Nursing Journal · 2020
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsMcGill UniversityMcGill University Health CentreJewish General Hospital
Fundersnot available
KeywordsPerceived controlEmpowermentControl (management)GerontologyPsychologyEnvironmental healthMedicineDevelopmental psychologyComputer scienceEconomic growthEconomicsArtificial intelligence

Abstract

fetched live from OpenAlex

BACKGROUND: Health-related empowerment is a key concept in person-centred care. However, little is known of its core elements in young adults diagnosed with advanced cancer. OBJECTIVE: To explore empowerment in the context of young adults' healthcare experiences who are now in advanced stages of cancer. SETTING & PARTICIPANTS: Twelve young adults (aged 21 to 39 years) were recruited from a large cancer centre in Montreal, Quebec. METHODS: In-depth interviews lasting between 36 and 90 minutes were conducted individually, audio-recorded, transcribed verbatim, and analyzed using thematic analysis. RESULTS: Throughout the cancer trajectory, participants reported a sustained desire to be actively involved in their treatment and care. Four themes emerged from the data representing processes of waiting, managing, acting, and revisiting. Subsumed under these were notions of body ownership, facing obstacles to care, optimizing health, and (re)considering their legacy. CONCLUSIONS: Overall, participants wanted to remain in control of their situation despite the multiple challenges related to advanced cancer. If corroborated further, these findings should inform supportive cancer care approaches that are truly tailored to the needs of young adults.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.612
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.020
GPT teacher head0.351
Teacher spread0.331 · 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 designObservational
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
Published2020
Admission routes3
Has abstractyes

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