LGBTQ Older Adult Recruitment in the Midst of a COVID-19 Lockdown: Reminiscences of a Post-Doctoral Fellow
Bibliographic record
Abstract
Abstract Within days of obtaining ethics approval for a qualitative study “Optimizing LGBTQ Engagement with Hospice and Palliative Care in the Island Health Region” our local Covid-19 lockdown began. It took several months to have new Covid-19 research protocols (Zoom Town Hall meetings/Zoom or telephone interviews) approved. Being impatient, I teamed with another group of researchers to launch “Covid-19: Your Current Experiences and Planning for the Future,” an online survey with a large qualitative component where we planned to oversample LGBTQ respondents. In time both projects were approved, and here I reflect on recruitment lessons learned. These include my perceptions how Zoom Town Hall meetings and interviews differ from those I’ve conducted in-person, reflections on how to use social media (including targeted Facebook advertising) to recruit participants, and sadly, how to manage anti-LGBTQ sentiment that resulted from even the most targeted advertising.
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 | 0.032 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.030 | 0.016 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.006 | 0.020 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".