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Record W7133072371

Understanding the care experiences and needs of hereditary cancer syndrome patients in Canada

2024· dissertation· W7133072371 on OpenAlexaffabout
Carly Butkowsky

Bibliographic record

VenueTSpace · 2024
Typedissertation
Language
FieldBiochemistry, Genetics and Molecular Biology
TopicBRCA gene mutations in cancer
Canadian institutionsInstitute of Health Services and Policy ResearchCanadian Institute for Health Information
Fundersnot available
KeywordsFeelingQualitative researchHealth careClinical PracticeLynch syndromeGenetic counselingThematic analysisCancer
DOInot available

Abstract

fetched live from OpenAlex

Aim: To explore the care experiences and needs of hereditary cancer syndrome (HCS) patients across Canada to inform clinical practice.Methods: Qualitative descriptive study consisting of semi-structured interviews with HCS patients who had a positive molecular diagnosis of hereditary breast and ovarian syndrome or Lynch syndrome. Results: Interviews were conducted with 73 patients. Participants described a sense of disorientation after their positive germline HCS diagnoses, with the sense of being lost, navigating a road without a map. These feelings emerged from the “fragmentation” of their care, their bodies, and information. Consequently, patients described experiencing a sense of uncertainty and distress, and desired integration in the form of consistent, knowledgeable healthcare practitioners and a streamlined, holistic approach to care. Conclusions: Patients described a range of needs that are relevant for clinical practice and policy. Integrating care by establishing provincial HCS programs could facilitate increased patient satisfaction and optimize care outcomes.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.067
Threshold uncertainty score0.300

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0120.004
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.021
GPT teacher head0.296
Teacher spread0.275 · 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

Citations0
Published2024
Admission routes2
Has abstractyes

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