Behaviorally informed interventions can increase take-up of public employment services, but conversion remains challenging: insights from an RCT in British Columbia, Canada
Bibliographic record
Abstract
Abstract Low take-up of government services continues to challenge public investments in social services. Behaviorally informed interventions, so-called nudges, can overcome barriers that keep eligible individuals from accessing services. We report results from a pre-registered randomized controlled trial (RCT) to test email-based interventions to increase the take-up of publicly funded employment services in British Columbia, Canada. Our RCT design distinguishes between getting people ‘to-the-door’ (awareness and interest) and ‘through-the-door’ (enrollment). We find that emails with concise information that route individuals directly to online enrollment are most effective. The best-performing interventions more than doubled enrollment within 14 days, relative to a control group that received no communication. Using machine learning identify subgroups within the population who benefit most from our interventions. Yet despite these positive effects on take-up, we find that converting expressions of interest into enrollments remains a challenge. To increase take-up, policymakers must identify the nature of the challenge: getting people to-the-door or through-the-door. We also contribute to current debates about the quality of public service delivery.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".