Perspectives of healthcare professionals on patient Internet use during the cancer experience
Bibliographic record
Abstract
In this study, we document cancer healthcare professionals' views of patients' use of cancer-related Internet information (CRII) and their views on how it informs the ways patients interact with healthcare professionals and services from the point of view of health professionals. We used an interpretive descriptive approach, conducting interviews and focus groups with oncology healthcare professionals (n = 21) at a University-affiliated western Canadian cancer treatment centre. Data were analysed using thematic analysis. We present an initial understanding of how CRII alters, informs and modulates patients' cancer experience and relates to their interactions with healthcare professionals and services. Findings were synthesised into two thematic categories: pragmatic concerns and priorities; and processes and practices. Healthcare professionals were supportive of patients' needs for more information, particularly at key points in the cancer trajectory when information may be lacking. Participants concurred that CRII could positively benefit patients and, if shared with their healthcare professional, could benefit the patient-healthcare professional relationship. Oncology healthcare professionals provide pivotal information to patients; thus, they are well situated to engage patients in discussions about CRII and incorporate this into patient encounters. These actions may open new lines of communication with patients, strengthen the patient-professional relationship and empower patients to be engaged in their own care.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".