Reconceptualizing Recruitment in Qualitative Research
Bibliographic record
Abstract
Adequate participant recruitment is critical for any qualitative research project. Our research team experienced numerous difficulties when attempting to recruit young adults with type 1 diabetes to discuss their transition from pediatric to adult-focused care. Using our experience as a case study, we identify the activities involved in four phases of participant recruitment: (1) development of a recruitment plan, (2) implementation, (3) participant engagement post-data collection, and (4) post-recruitment assessment. We present a new definition of participant recruitment which better captures the range of activities involved. We discuss aspects impacting recruitment in our case: the influence of other stakeholders, the dynamic nature of recruitment, recruitment of specific populations, and the challenges of recruiting within a healthcare environment. Finally, we identify and consider four factors that impact participant recruitment: communication, participant interest/value, participant trust in the research project, and participant availability and consider potential strategies for overcoming barriers related to each factor. In the end, our case underscores the centrality and potential fluidity of participant recruitment within qualitative research.
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 | 0.802 | 0.761 |
| Meta-epidemiology (narrow) | 0.004 | 0.006 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.015 | 0.010 |
| Science and technology studies | 0.022 | 0.083 |
| Scholarly communication | 0.034 | 0.037 |
| Open science | 0.015 | 0.050 |
| Research integrity | 0.015 | 0.023 |
| Insufficient payload (model declined to judge) | 0.007 | 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; 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".