The Impact of Participation in Online Cancer Communities on Patient Reported Outcomes: Systematic Review
Bibliographic record
Abstract
BACKGROUND: In recent years, the question of how patients' participating in online communities affects various patient reported outcomes (PROs) has been investigated in several ways. OBJECTIVES: This study aimed to systematically review all relevant literature identified using key search terms, with regard to, first, changes in PROs for cancer patients who participate in online communities and, second, the characteristics of patients who report such effects. METHODS: A computerized search of the literature via PubMed (MEDLINE), PsycINFO (5 and 4 stars), Cochrane Central Register of Controlled Trials, and ScienceDirect was performed. Last search was conducted in June 2017. Studies with the following terms were included: (cancer patient) and (support group or health communities) and (online or Internet). A total of 21 studies were included and independently assessed by 2 investigators using an 11-item quality checklist. RESULTS: The methodological quality of the selected studies varied: 12 were of high quality, eight were of adequate quality, and only one was of low quality. Most of the respondents were women (about 80%), most with breast cancer; their mean age was 50 years. The patients who were active in online support groups were mostly younger and more highly educated than the nonusers. The investigated PROs included general well-being (ie, mood and health), anxiety, depression, quality of life, posttraumatic growth, and cancer-related concerns. Only marginal effects-that is, PRO improvements-were found; in most cases they were insignificant, and in some cases they were contradictory. CONCLUSIONS: The main shortcoming of this kind of study is the lack of methodological instruments for reliable measurements. Furthermore, some patients who participate in online communities or interact with peers via Internet do not expect to measure changes in their PROs. If cancer survivors want to meet other survivors and share information or get support, online communities can be a trustworthy and reliable platform to facilitate opportunities or possibilities to make this happen.
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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 | 0.016 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".