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.
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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 | 0.038 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".