Tailoring research recruitment strategies to survey harder‐to‐reach populations: A discussion paper
Bibliographic record
Abstract
AIMS: A discussion of the challenges of recruiting participants from harder-to-reach populations for quantitative survey studies and potential avenues for tailored strategies to address these challenges. DESIGN: Discussion paper. DATA SOURCES: The search was conducted on August 2, 2021, in the CINAHL and PubMed databases, and in Google scholar. The initial search identified 5880 articles, and the final analysis included 44 articles that met the inclusion criteria. Articles were retained if they addressed methodological challenges or strategies for recruitment and concerned research with harder-to-reach populations. IMPLICATIONS FOR NURSING: This article draws on the literature regarding the challenges of recruiting research participants from harder-to-reach populations and known strategies for overcoming them. These strategies include, for example, establishing a trusting relationship between the researcher and the participant community and gaining in-depth knowledge of the target population. These challenges and strategies for recruiting participants from these populations are discussed specifically in the context of quantitative survey research. CONCLUSION: Nurse researchers conducting quantitative survey studies with participants from harder-to-reach populations must tailor their recruitment strategies to the target population and, most importantly, be flexible and creative in their recruitment methods. IMPACT: The article discusses the challenges of recruiting participants from harder-to-reach populations and strategies to overcome them in quantitative survey studies. Successful recruitment requires researchers to develop a thorough understanding of the harder-to-reach population, develop partnerships to locate and access potential participants, build trust with the community, tailor their language, minimize participation risk and resource constraints, recognize the cognitive and physical demands required, and be flexible and creative in developing recruitment strategies. This knowledge can enable the inclusion of more people from harder-to-reach populations in survey studies and provide evidence that can inform research and practice to provide healthcare tailored to their needs and ultimately help improve their health and well-being.
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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 | 0.016 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".