Making a Survey For Psychology Research: The Process
Bibliographic record
Abstract
Throughout the summer, I helped with two separate studies which were in different stages of completion: The Healthcare Workers Mental Health During COVID-19 (HCW) Survey, and the Canadian Youth Vaccine Confidence (CYVC) Survey. The HCW survey consisted of tasks involving making engaging posters to advertise using Canva, contacting facebook and twitter groups and health organizations with proper email formatting, and using qualtrics to sort and send gift cards to participants efficiently. Overall, my group collected data from BC and ON healthcare workers (hospitals, residential, mental health, and other healthcare jobs). The CYVC Survey was a different type of research since I participated in creating the survey. Tasks involved with this survey included learning about gathering and using standardized scales (such as PHO-2 for depression, GAD-2 for anxiety, intolerance of uncertainty scale, and questions about COVID-19 stress just to name a few) to ask youth about their thoughts on the COVID-19 Vaccine. I also assisted with filling out an ethics application connected through UBC harmonized ethics. This ethics form describes all the aspects of the research being done as well as the purpose, and who’s involved. This ensures that both participants and researchers are protected.
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 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 | 0.186 | 0.286 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.004 | 0.012 |
| Insufficient payload (model declined to judge) | 0.106 | 0.079 |
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".