Information needs of patients with lung cancer from diagnosis until first treatment follow-up
Bibliographic record
Abstract
The aim of this study was to analyze the information needs of lung cancer patients from diagnosis until first treatment follow-up. Sixty-nine participants with lung cancer were recruited from Ditmanson Medical Foundation Chia-Yi Christian Hospital in Midwest Taiwan. The Modified Toronto Informational Needs Questionnaire (TINQ) was used to assess information needs during visits to the outpatient oncology department. Generalized estimating equations were applied to compare changes in information needs over time and to examine correlates of information needs of lung cancer patients. The greatest concern of lung cancer patients was the cancer itself and access to recovery information. The need for information regarding food selection and social welfare resources was also high. However, the means of information needs for each domain significantly decreased over time. Demographic information (age, gender, disease stage, current treatment, education, work status, and having children) was significantly associated with information needs over time. The need for "disease-related information" remained high regardless of disease stage. Oncology nurses can use the results of this study to better address the information needs of patients in an effort to fill knowledge gaps between patients and healthcare providers. Further studies are needed to explore the use of an appropriate instrument, like that used in this study, to identify newly-diagnosed lung cancer patients' difficulties, concerns, and target interventions to improve their quality of life.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".