When Limited Clinical Time With Patients Meets Unlimited Online Information
Bibliographic record
Abstract
As patients with cancer increasingly seek guidance from online sources, the patient-clinician relationship is at risk of being displaced by fragmented, often unreliable information. One of the primary drivers of this trend is the insufficient time available for in-depth, relational consultation with health care providers (HCPs). We argue that the current clinical routine, constrained by documentation and administrative demands, fails to allow adequate time for supporting the informational, emotional, and relational needs of patients navigating complex decisions. This shortfall undermines HCPs' ability to engage patients in shared decision-making and weakens the foundation of trust between patient and HCP. For some patients, this can result in selecting less-effective treatments or turning away from evidence-based care toward unproven online alternatives. While policy reforms to reduce administrative burdens and free up time for patient education and counseling are essential, they are slow to materialize, making immediate, actionable steps at the clinician level more urgent. We propose a set of practical, evidence-informed strategies that clinicians can adopt today to help meet patients' informational and emotional needs, strengthen patient-HCP relationships, and ensure that patients' health care decisions fit their preferences and are supported by scientific evidence.
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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 | 0.012 | 0.092 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.044 | 0.007 |
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".