Efficient Online Recruitment of Patients With Depressive Symptoms Using Social Media: Cross-Sectional Observational Study
Bibliographic record
Abstract
BACKGROUND: Over 80% of trials worldwide fail to complete patient recruitment within the initially planned time frame. Over the past decade, the use of social media for recruitment in medical research has become increasingly popular. While Google and Facebook are well established, newer social media channels such as Instagram and TikTok garner less research attention as recruitment tools. Although some studies have investigated the advantages and disadvantages of using social media for recruitment, a considerable gap still exists in understanding the precise mechanisms and factors that make different social media platforms most effective and cost-efficient for patient recruitment in mental health studies. OBJECTIVE: This study evaluates the effectiveness of recruitment strategies implemented during the investigative phase of a validation study for a new suicidality assessment questionnaire optimized for primary care. METHODS: We describe how online recruitment contributed to the enrollment of patients with depressive symptoms for the validation of a suicidality questionnaire (Suicide Prevention in Primary Care), which required over 500 participants. To this end, we analyzed differences in sample demographics between traditionally recruited and online participants, compared advertising metrics and conversion rates, and conducted a cost-benefit analysis. RESULTS: We found online recruitment to be a fast and efficient method of securing the required number of participants with depressive symptoms for the study and increasing patient diversity. Considering the distribution of gender, age, and Patient Health Questionnaire-9 scores, participants recruited offline and online were equally eligible for the study. Online recruitment demonstrated high advertising efficiency. For example, the study population responded well to video advertisements on social media; these performed 50% to 70% more cost-efficiently than the best image advertisements. Moreover, a long website copy proved slightly better than a short version. Pixel tracking for improved advertisement targeting reduced advertising costs per suitable participant by 83.3%, making the advertisements 6 times more cost-efficient. CONCLUSIONS: Social media recruitment increased the diversity of patients in the studies and proved suitable for vulnerable and hard-to-reach populations. The total cost per patient recruited online was comparable to that achieved using offline methods, but overall recruitment progressed faster. In this study, implementing video advertisements and pixel tracking resulted in significant cost savings.
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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".