Recruiting primary care physicians to qualitative research: Experiences and recommendations from a childhood cancer survivorship study
Bibliographic record
Abstract
BACKGROUND: Primary care physicians (PCPs) are essential for healthcare delivery but can be difficult to recruit to health research. Low response rates may impact the quality and value of data collected. This paper outlines participant and study design factors associated with increased response rates among PCPs invited to participate in a qualitative study at Sydney Children's Hospital, Australia. PROCEDURE: We invited 160 PCPs by post, who were nominated by their childhood cancer patients in a survey study. We followed-up by telephone, email, or fax 2 weeks later. RESULTS: Without any follow-up, 32 PCPs opted in to the study. With follow-up, a further 42 PCPs opted in, with email appearing to be the most effective method, yielding a total of 74 PCPs opting in (46.3%). We reached data saturation after 51 interviews. On average, it took 34.6 days from mail-out to interview completion. Nonrespondents were more likely to be male (P = 0.013). No survivor-related factors significantly influenced PCPs' likelihood of participating. Almost double the number of interviews were successfully completed if scheduled via email versus phone. Those requiring no follow-up did not differ significantly to late respondents in demographic/survivor-related characteristics. CONCLUSION: PCP factors associated with higher opt in rates, and early responses, may be of interest to others considering engaging PCPs and/or their patients in cancer-related research, particularly qualitative or mixed-methods studies. Study resources may be best allocated to email follow-up, incentives, and personalization of study documents linking PCPs to patients. These efforts may improve PCP participation and the representativeness of study findings.
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 | 0.159 | 0.141 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.020 | 0.016 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.006 | 0.006 |
| 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; 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".