COVID-19 and Mental (ill) Health Sass Class
Bibliographic record
Abstract
It's time for the Super Awesome Science Show SASS Class on COVID-19 and its effects on our healthcare heroines.I want to thank everyone who reached out to me. We received quite a few and will try to answer them today.Our guest is Emily Jenkins. Emily Jenkins. She is an Assistant Professor at the School of Nursing at the University of British Columbia. She is focused on optimizing mental health outcomes for Canadians through collaborative mental health promotion strategies; health services and policy development and redesign; and knowledge translation approaches. She has also reached out to Canadians and learned about how they really feel about this pandemic. Her two papers on the subject can be found below.If you didn't hear your question, make sure to contact me on Twitter, by Email and now, via voice message at Speakpipe.com/SASS. Just follow the link below and send me your thoughts. Twitter: @JATetroEmail: thegermguy@gmail.comGuest: Emily JenkinsEmily Jenkins, PhD, MPH, RN | School of Nursing (ubc.ca)COVID-19 and Individual Mental HealthA portrait of the early and differential mental health impacts of the COVID-19 pandemic in Canada: Findings from the first wave of a nationally representative cross-sectional survey - ScienceDirectCOVID-19 and Family Mental HealthExamining the impacts of the COVID-19 pandemic on family mental health in Canada: findings from a national cross-sectional study | BMJ OpenLearn more about your ad choices. Visit megaphone.fm/adchoices
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.461 | 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; both teacher heads agree on what is shown here.
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".