Challenges in recruiting hard-to-reach populations focusing on Latin American recent immigrants
Bibliographic record
Abstract
Purpose – Recruiting immigrant populations, particularly recent arrivals, is challenging due to lack of sampling frames and other factors. The purpose of this paper is to report the feasibility of using a quasi-random sampling strategy for recruiting recent Latin American (LA) immigrants. Design/methodology/approach – The initial recruitment strategy included random selection of two census tracts (CTs) with high concentrations and numbers of recent LAs in Toronto, and door-to-door recruitment. Based on challenges encountered this strategy was modified by consulting trusted community members and recruiting participants residing in selected CTs using cultural venues. Findings – Door-to-door recruitment of the target group is difficult. Challenges included accessing individuals living in apartment buildings, lack of trust and fear of deportation, transitory residency, and difficulty recruiting very recent arrivals. The modified strategy was more efficient and yielded higher recruitment rates, and was more acceptable to participants. Research limitations/implications – The limited timeframe of the study and lack of timely census data may have prevented full exploration of study methodologies. Originality/value – The study demonstrated that recruitment rates of recent immigrants and refugees can be improved by randomly selecting CTs with high concentrations and numbers of recent immigrants and using culturally appropriate recruitment strategies. These groups may not be homogeneously distributed in selected geographic areas (e.g. CTs); it may be necessary to focus on pockets of high concentration as identified by community members who are familiar with the area.
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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 | 0.056 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".