Recruiting hard-to-reach populations to physical activity studies: evidence and experiences
Bibliographic record
Abstract
Most researchers who are conducting research with a public health focus face difficulties in recruiting the segments of the population that they really want to reach. This symposium presented evidence and experiences on recruiting participants to physical activity research, including both epidemiological and intervention based studies. Results from a systematic review of recruitment strategies suggested that we know little about how best to recruit and highlighted the need for researchers to report this in more detail, including metrics of reach into the target population such as number, proportion, and representativeness of participants. Specific strategies used to optimise responses to a population-based mail survey were presented such as study promotion, survey design, multiple mailings, and personal engagement. Finally, using place based recruiting via schools or places of worship to target ethnic minority youth were discussed. Overall the symposium presenters suggested that we need to learn more about how best to recruit participants, in particular those typically under-represented, and that researchers need to apportion a similar amount of planning effort to their recruitment strategies as they do the their research design. Finally we made a plea for researchers to report their recruitment processes in detail.
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 | 0.262 | 0.344 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier 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".