The Feasibility of Using the National PulsePoint Cardiopulmonary Resuscitation Responder Network to Facilitate Overdose Education and Naloxone Distribution: Protocol for a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: The use of naloxone, an opioid antagonist, is a critical component of the US response to fatal opioid-involved overdoses. The importance and utility of naloxone in preventing fatal overdoses have been widely declaimed by medical associations and government officials and are supported by strong research evidence. Still, there are gaps in the current US national strategy because many opioid-involved overdose fatalities have no evidence of naloxone administration. Improving the likelihood that naloxone will be used to prevent fatal overdoses is predicated on facilitating an environment wherein naloxone is available near each overdose and can be accessed by someone who is willing and able to use it. How to accomplish this on a national scale has been unclear. However, there exists a national network of >1 million cardiopulmonary resuscitation (CPR) layperson responders and 4800 emergency responder agencies linked through a mobile phone app called PulsePoint Respond. PulsePoint responders certify that they are trained to administer CPR and are willing to respond to possible cardiac events in public. When such an event occurs near their mobile phone's location, they receive an alert to respond. These motivated citizens are ideally positioned to carry naloxone and reverse overdoses that occur in public. OBJECTIVE: This randomized controlled trial will examine the feasibility of recruiting first responder agencies and layperson CPR responders who already use PulsePoint to obtain overdose education and carry naloxone. METHODS: This will be a 3-arm parallel-group randomized controlled trial. We will randomly select 180 first responder agencies from the population of agencies contracting with the PulsePoint Foundation. The 3 study arms will include a standard recruitment arm, a misperception-correction recruitment arm, and a control arm (1:1:1 allocation, with random allocation stratified by zip code designation [rural or nonrural]). We will study agency recruitment and, among the agencies we successfully recruit, responder certification of receiving overdose and naloxone education, carrying naloxone, or both. Hypothesis 1 contrasts agency recruitment success between arms 1 and 2, and hypothesis 2 contrasts the ratios of layperson certification across all 3 arms. The primary analyses will be a logistic regression comparing the recruitment rates among the arms, adjusting for rural or nonrural zip code designation. RESULTS: This study was reviewed by the Indiana University Institutional Review Board (20218 and 20219). This project was funded beginning September 14, 2023, by the National Institute on Drug Abuse. CONCLUSIONS: The hypotheses in this study will test whether a specific type of messaging is particularly effective in recruiting agencies and layperson responders. Although we hypothesize that arm 2 will outperform the other arms, our intention is to use the best-performing approach in the next phase of this study if any of our approaches demonstrates feasibility. TRIAL REGISTRATION: OSF Registries osf.io/egn3z; https://osf.io/egn3z. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/57280.
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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 | 0.046 | 0.052 |
| Meta-epidemiology (narrow) | 0.007 | 0.003 |
| Meta-epidemiology (broad) | 0.012 | 0.007 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.073 | 0.011 |
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".