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Record W2789562490 · doi:10.1177/2333393618760337

Experiences of Head and Neck Cancer Patients in Middle Adulthood: Consequences and Coping

2018· article· en· W2789562490 on OpenAlexaff
Kathryn Grattan, Catherine Kubrak, Vera Caine, Dan O’Connell, Kärin Olson

Bibliographic record

VenueGlobal Qualitative Nursing Research · 2018
Typearticle
Languageen
FieldMedicine
TopicHead and Neck Cancer Studies
Canadian institutionsSt Joseph's Health CareUniversity of Alberta
Fundersnot available
KeywordsCoping (psychology)FeelingHead and neck cancerPsychologyPsychological interventionSocial supportClinical psychologyPopulationInterpersonal communicationMedicinePsychotherapistSocial psychologyCancerPsychiatry

Abstract

fetched live from OpenAlex

The head and neck cancer (HNC) rate is rising among the middle-aged adult population. This trend has been attributed primarily to human papillomavirus exposure. An HNC diagnosis and its complex treatments may trigger life-changing physical, emotional, and social consequences. An interpretive descriptive study was conducted to describe the experiences of a purposive sample of 10 middle-aged adults who had experienced HNC. Two main themes were identified: consequences of HNC and coping with HNC. Subthemes of consequences of HNC included: voicelessness; being or looking sick; shifts in family dynamics; and sexual practices, sexual feelings, and stigma. Subthemes of coping with HNC included seeking information, discovering inner strengths, relying on a support network, establishing a sense of normalcy, and finding meaning within the experience. Supportive nursing interventions were identified by considering results from the standpoint of King's theory of goal attainment.

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)0.002
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 score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0040.002
Scholarly communication0.0030.002
Open science0.0000.004
Research integrity0.0010.001
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 head0.200
GPT teacher head0.542
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 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

Citations26
Published2018
Admission routes1
Has abstractyes

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