The Australian Longitudinal Study on Women's Health: Using Focus Groups to Inform Recruitment
Bibliographic record
Abstract
BACKGROUND: Recruitment and retention of participants to large-scale, longitudinal studies can be a challenge, particularly when trying to target young women. Qualitative inquiries with members of the target population can prove valuable in assisting with the development of effective recruiting techniques. Researchers in the current study made use of focus group methodology to identify how to encourage young women aged 18-23 to participate in a national cohort online survey. OBJECTIVE: Our objectives were to gain insight into how to encourage young women to participate in a large-scale, longitudinal health survey, as well as to evaluate the survey instrument and mode of administration. METHODS: The Australian Longitudinal Study on Women's Health used focus group methodology to learn how to encourage young women to participate in a large-scale, longitudinal Web-based health survey and to evaluate the survey instrument and mode of administration. Nineteen groups, involving 75 women aged 18-23 years, were held in remote, regional, and urban areas of New South Wales and Queensland. RESULTS: Focus groups were held in 2 stages, with discussions lasting from 19 minutes to over 1 hour. The focus groups allowed concord to be reached regarding survey promotion using social media, why personal information was needed, strategies to ensure confidentiality, how best to ask sensitive questions, and survey design for ease of completion. Recruitment into the focus groups proved difficult: the groups varied in size between 1 and 8 participants, with the majority conducted with 2 participants. CONCLUSIONS: Intense recruitment efforts and variation in final focus group numbers highlights the "hard to reach" character of young women. However, the benefits of conducting focus group discussions as a preparatory stage to the recruitment of a large cohort for a longitudinal Web-based health survey were upheld.
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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 | 0.124 | 0.128 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".