Examining the Development of Information Needs Assessment Questionnaires in Oncology: Protocol for a Scoping Review
Bibliographic record
Abstract
BACKGROUND: Information needs are one of the most prevalent unmet supportive care needs of those living with cancer, including patients and their informal caregivers. Understanding how existing questionnaires for evaluating information needs have been developed is important for guiding appropriate use and informing future research. A literature review examining how information needs assessment questionnaires for use in the cancer context have been developed, with a specific focus on how questionnaire items have been identified, does not exist. OBJECTIVE: This scoping review will examine how questionnaires for assessing the information needs of those living with cancer have been developed with special focus on how patients, informal caregivers, and health care professionals have been involved in the selection and identification of questionnaire items. METHODS: This review will include published studies describing the development and validation of information needs assessment questionnaires for use in the oncology context. MEDLINE (Ovid), Embase (Ovid), CINAHL, Scopus, Web of Science, the Cochrane Database of Systematic Reviews, and PsycInfo will be searched. Articles published at any point up to the date of the search will be eligible for inclusion. One person will screen titles and abstracts, and 2 people will screen and extract data from full-text articles. RESULTS: Results are expected to be available in early 2023. Summary tables and a narrative summary will be used to describe results. CONCLUSIONS: This scoping review will assist in identifying appropriate information needs assessment tools to incorporate into clinical and research contexts in oncology. It will also identify if additional information needs assessment tools are needed. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/35639.
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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 | 0.129 | 0.130 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.012 | 0.015 |
| Bibliometrics | 0.018 | 0.017 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.077 | 0.015 |
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".