Using Facebook and LinkedIn to Recruit Nurses for an Online Survey
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Social media is an emerging tool used by researchers; however, limited information is available on its use for participant recruitment specifically. The purpose of this article is to describe the use of Facebook and LinkedIn social media sites in the recruitment of nurses for an online survey, using a 5-week modified online Dillman approach. Within 3 weeks, we exceeded our target sample size ( n = 170) and within 5 weeks recruited 267 English-speaking nurses ( n = 172, Facebook; n = 95, LinkedIn). Advantages included speed of recruitment, cost-efficiency, snowballing effects, and accessibility of the researcher to potential participants. However, an analysis of the recruited participants revealed significant differences when comparing the sociodemographics of participants recruited through Facebook and LinkedIn, specifically relating to the characteristics of sex, age, and level of education. Differences between Facebook and LinkedIn as recruitment platforms should be considered when incorporating these strategies.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it