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Record W2087004714 · doi:10.5737/1181912x1711620

Information needs of adolescents when a mother is diagnosed with breast cancer

2007· article· en· W2087004714 on OpenAlexaffvenue
Margaret I. Fitch, Tara Abramson

Bibliographic record

VenueCanadian Oncology Nursing Journal · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicFamily Support in Illness
Canadian institutionsSunnybrook Health Science Centre
Fundersnot available
KeywordsBreast cancerInformation needsMedicineCancerPsychologyFamily medicineInternal medicineComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

This pilot study was undertaken to increase our understanding of the information needs of adolescents when a mother is diagnosed with breast cancer. In-depth interviews with 13 adolescents provided insight into the types of questions they had at the time of their mother's diagnosis and how satisfied they were with the access they had to information. These adolescents had many questions about cancer and its treatment, specific questions about their mother's disease and survival, and concerns about their own risk. Each sought information on their own in addition to conversations with one or both parents. They identified their most pressing concern as the need to know about their mother's survival. All felt it was important to have access to information and to have someone with whom they could talk about what was happening. This person needed to be someone with whom they were comfortable and whom, in turn, had both credibility and comfort with emotions. Clearly, adolescents experience needs for information when their mother is diagnosed with breast cancer. Cancer nurses can assist women diagnosed with breast cancer plan how to support their adolescent children and meet the needs for information.

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)0.008
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.014
GPT teacher head0.302
Teacher spread0.288 · 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
Published2007
Admission routes2
Has abstractyes

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