Cancer informational support and health care service use among individuals newly diagnosed: a mixed methods approach
Bibliographic record
Abstract
AIM: To report on the integration of quantitative and qualitative findings to increase understanding of the role of cancer informational support and use of health care services among individuals newly diagnosed with breast or prostate cancer. METHODS: A mixed methods sequential design was used. First, a quantitative secondary analysis considered self-report data from a large number of individuals newly diagnosed with cancer (n = 250); next, a follow-up, in-depth qualitative inquiry with distinct individuals also newly diagnosed was conducted (n = 20); last, using a quantitative-hierarchical strategy, quantitative and qualitative findings were merged and re-analyzed. RESULTS: Quantitative analyses showed significant relationships between informational support and health care services. For instance, individuals who received more intense cancer informational support [face-to-face and information technology (IT)] spent more time with nurses. Women with breast cancer as opposed to men with prostate cancer also were found to rely primarily on nurses for cancer information and information on health services available, whereas men relied mostly on their oncologists. In-depth interviews revealed that informational support could be construed as positive, unsupportive, or mixed depending on context. The mixed design analysis documented positive experiences for individuals who reported to be better prepared for consultations and treatments with information provided by more than one source. Negative experiences with physicians were reported by both women and men but the former was about quality of cancer information provided and the latter in terms of quantity. CONCLUSIONS: A mixed methods approach allowed a deeper understanding of the role of informational support on subsequent use of health care services by individuals with cancer. Further studies may include other types of cancer and diverse background characteristics to clarify how informational support and subsequent use of health services may be jointly determined by these factors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".