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Record W2939052411 · doi:10.1177/2374373519831700

Exploring Experiences of Survivors and Caregivers Regarding Lung Cancer Diagnosis, Treatment, and Survivorship

2019· article· en· W2939052411 on OpenAlexafffund
Margaret I. Fitch

Bibliographic record

VenueJournal of Patient Experience · 2019
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsUniversity of Toronto
FundersLung Health Foundation
KeywordsSurvivorship curveCancer survivorshipLung cancerMedicineGerontologyCancerPsychologyOncologyInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Advances in screening and treatment approaches alongside changing population demographics have the potential to influence the experience of living with lung cancer. There is potential for improved outcomes and quality of life for those diagnosed with the disease. OBJECTIVES: This exploratory study was undertaken to gain insight regarding the current experiences of individuals diagnosed with lung cancer and their family caregivers given the evolving changes in lung cancer screening and treatment. METHOD: A qualitative descriptive design was utilized and in-depth interviews conducted with 8 survivor and 4 family caregivers. Interviews were subjected to a conventional content analysis. RESULTS: Participants identified challenges related to being diagnosed in a timely manner, being told the diagnosis with compassion, coping with multiple symptoms during treatment, and regaining a new normal following treatment. Dealing with late effects of treatment (ie, fatigue, shortness of breath, neuropathy) was frustrating when individuals were not aware the effects would emerge or had not had relevant self-management instructions. CONCLUSIONS: Lung cancer survivors constitute an emerging cadre of survivors. Attention is needed to their preparation for, and coping with, the survivorship transition.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.623
Threshold uncertainty score0.561

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 studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.047
GPT teacher head0.298
Teacher spread0.251 · 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.

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

Citations14
Published2019
Admission routes2
Has abstractyes

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