What Are the Unmet Information Needs of Cancer Patients? A Qualitative Study
Bibliographic record
Abstract
OBJECTIVE: Identify the unmet information needs of cancer patients and understand the causes of patients’ dissatisfactionMETHODS: A qualitative method using interviews with cancer patients attending a Meeting and Information Area (ERI) at Gustave Roussy cancer center (France), and focus groups with the ERI professionals. The data were analysed using vertical and horizontal open coding.RESULTS: Firstly, the needs for medical information are important, but there are other types of information that patients need (e.g. organizational information). Secondly, patients’ dissatisfaction is not linked only to the lack of medical information; it also reflects other needs, which are not taken into account (e.g. accompanying information to make it understandable and useful). Thirdly, the relationships established over time between patients and professionals make possible the emergence of latent needs (ranging from basic information needs to requests for psychological support).CONCLUSION: Information must be considered in an integrated and holistic approach to facilitate patients’ navigation and improve their health literacy.PRACTICE IMPLICATIONS: The training of healthcare professionals is crucial, but this is not enough. The introduction of other, non-carers professionals is necessary to address a broad range of patients’ needs in a more effective and cost-efficient way.
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 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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".