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Record W7117701444 · doi:10.1155/ecc/1098567

Listen to Lung Cancer Patients’ Emotions With Photographs Taken by Them: A Mixed‐Method Study

2025· article· en· W7117701444 on OpenAlexfundno aff
Aysun Akcakaya Can, Sevilay Hintistan, Yilmaz Bulbul

Bibliographic record

VenueEuropean Journal of Cancer Care · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsnot available
FundersTürkiye Bilimsel ve Teknolojik Araştırma KurumuRyerson University
KeywordsLung cancerPhotovoiceQualitative researchScale (ratio)CancerIntervention (counseling)Sampling (signal processing)

Abstract

fetched live from OpenAlex

Aim This study was conducted to examine the emotional experiences of lung cancer patients. Methods This study, carried out with an intervention design, is a mixed‐method study in two parts. The qualitative part of the mixed method was enriched with photovoice methodology. The study used a criterion sampling technique, and 17 participants were interviewed. Some scales collected quantitative data from the survey, and qualitative data were collected through “semistructured interviews” and “photographs.” Results The themes in the first part are “meeting with cancer and a new life.” The second part determines the “range of emotions, the world through my window, and perspective.” The study revealed in striking detail that lung cancer patients experience many different emotions simultaneously. There was no significant difference in the scale mean scores of the participants. Conclusion Phototherapy is essential for revealing lung cancer patients’ emotions and concentrating on positive emotions. Cancer nurses can also add phototherapy as a simple, feasible, and accessible method of caring for lung cancer patients. Thus, lung cancer patients can participate in life through the photographs they take.

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)0.003
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.219
Threshold uncertainty score0.515

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.197
GPT teacher head0.573
Teacher spread0.377 · 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 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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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