Implementation of Medication Disposal Programs and Availability of Same-Day Naloxone at Community Pharmacies: Protocol for a Secret Shopper Caller Approach
Notice bibliographique
Résumé
BACKGROUND: Pharmacies can implement multiple strategies, including medication disposal programs (eg, disposal boxes, deactivation products, and mail-back envelopes) and offering over-the-counter naloxone, to prevent nonmedical opioid use and overdose. The quantity of opioid prescriptions dispensed in the United States is so high that every other adult could receive one opioid prescription per year. Many of these opioids go unused and are kept in homes rather than disposed of after ceasing use. The primary source of prescription opioids for nonmedical use is relatives or friends, which suggests that the diversion of excess and retained prescription opioids contributes significantly to nonmedical use. Naloxone is a life-saving medication that works as an opioid antagonist to reverse the effects of opioids and restore normal breathing to a person experiencing an overdose. All 50 US states have passed laws (eg, statewide standing orders) that allow pharmacists to distribute naloxone without an individual patient prescription, and the US Food and Drug Administration approved the first over-the-counter naloxone medication in March 2023. Individual and neighborhood characteristics are associated with nonmedical opioid use and overdose. It is essential to ensure that pharmacy-based overdose prevention practices are widely available to all individuals. OBJECTIVE: : This study aims to assess the extent to which disposal programs and same-day naloxone have been implemented in pharmacies across the United States and examine neighborhood characteristics in implementation. We hypothesize that as neighborhood disadvantage and the proportion of Black or African American residents in a neighborhood increase, the likelihood of a pharmacy having a disposal program or same-day naloxone decreases. We also hypothesize differences in medication disposal programs and same-day naloxone availability by retailer chain and type of pharmacy. METHODS: A secret shopper caller protocol will be used to identify pharmacies that have implemented a medication disposal program and have naloxone available on the same day without a prescription. We will conduct disproportionate stratified random sampling with the strata being pharmacy chains to maximize the likelihood of sampling corporations and independent pharmacies. The goal is to obtain a final sample of 1000 pharmacies. Neighborhood characteristics will be appended to the secret shopper data. To explore neighborhood and pharmacy characteristics associated with the availability of medication disposal programs and same-day naloxone, we will use logistic regression. This protocol represents the entire structure of the secret shopper caller approach. RESULTS: Data collection was completed in the spring of 2024. The expected results will be published in 2025. CONCLUSIONS: This will be the first study to examine national estimates of medication disposal programs, same-day naloxone availability at pharmacies, and the geographic characteristics associated with their implementation. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/64344.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,069 | 0,052 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,004 |
| Méta-épidémiologie (sens large) | 0,004 | 0,006 |
| Bibliométrie | 0,005 | 0,004 |
| Études des sciences et des technologies | 0,010 | 0,003 |
| Communication savante | 0,005 | 0,006 |
| Science ouverte | 0,006 | 0,007 |
| Intégrité de la recherche | 0,007 | 0,010 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,065 | 0,016 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».