Keeping pace with SOCial media technology: Implications for the mental health of individuals with neurological conditions
Bibliographic record
Abstract
The ever-changing face of technology makes it difficult for anyone to keep up with the latest and greatest offered to consumers.With advances in social media, the means by which people can communicate with others electronically has grown exponentially in the last decade.Wikipedia, Twitter, YouTube, Skype, Google+ and Facebook are now household names.This has important implications for those living with chronic illness, given that many health consumers actively seek information, advice and support one another via online venues. 1 In fact, numerous support organizations for individuals with neurological conditions now have their own Twitter, YouTube and Facebook pages.Patient groups, such as those in Canada with multiple sclerosis, have already provided a concrete example of how social media technologies can be used to influence research priorities and how research findings should be disseminated. 2So, what impact can these technologies have on mental health?To begin to address this question, we will draw on examples and reflections from our current doctoral research programs.
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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 | 0.017 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.023 |
| Scholarly communication | 0.017 | 0.020 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".