Cancer-related electronic support groups as navigation-aids: Overcoming geographic barriers\n
Bibliographic record
Abstract
Cancer-related electronic support groups (ESGs) may be regarded as a complement to face-to-face groups when the latter are available, and as an alternative when they are not. Advantages over face-to-face groups include an absence of barriers imposed by geographic location, opportunities for anonymity that permit sensitive issues to be discussed, and opportunities to find peers online. ESGs can be especially valuable as navigation aids for those trying to find a way through the healthcare system and as a guide to the cancer journey. Outcome indicators that could be used to evaluate the quality of ESGs as navigation aids need to be developed and tested. Conceptual models for the navigator role, such as the Facilitating Navigator Model, are appropriate for ESGs designed specifically for research purposes. A Shared or Tacit Model may be more appropriate for unmoderated ESGs. Both conceptual models raise issues in Internet research ethics that need to be address
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".