Evidence map›Paper›PMID 31556665›Full record

ArticleJournal of consulting and clinical psychology2019

A new OPIATE (Optimizing Positive Ibuprofen and Acetaminophen Treatment Expectations) model: A brief comment on "Concurrent and lagged associations of prescription opioid use with pain and negative affect in the daily lives of chronic pain patients" (Carpenter, Lane, Bruehl, & Trull, 2019).

Jack Stevens, Tarun Bhalla

Abstract readComment
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In one paragraph

Article in Journal of consulting and clinical psychology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

2 authors.

Jack StevensCenter for Biobehavioral Health, Nationwide Children's Hospital.
Tarun BhallaDepartment of Anesthesia and Pain Medicine, Akron Children's Hospital.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThis commentary expands upon the clinical implications arising from the Carpenter et al. (2019) negative reinforcement model of opioid use. A new model called OPIATE-which stands for Optimizing Positive Ibuprofen and Acetaminophen Treatment Expectations-is introduced. This commentary highlights possible strategies for reducing the supply and demand of opioids through enhancing expectations for alternative pain medications.

methodThis commentary features an integration of concepts from social psychology, cognitive psychology, and consumer psychology with empirical findings from observational studies and randomized trials of opioid and nonopioid medications.

resultsFour behavioral strategies are offered for potential enhancing expectations for ibuprofen and acetaminophen. These strategies are (a) altering defaults in electronic medical record systems; (b) highlighting appropriate descriptive social norms to patients; (c) improving the quality of interactions during discharges between patients and their physicians, nurses, and pharmacists; and (d) priming patients to have positive expectations regarding ibuprofen and acetaminophen involving careful selection of language to describe these medications.

conclusionsInstead of creating alternative pharmaceuticals, efforts to prevent opioid misuse may be more fruitful by focusing on behavioral change of prescribers and patients because viable medication options-namely ibuprofen and acetaminophen-already exist. Psychologists have an opportunity to play a leading role in evaluating behavioral strategies for increasing expectations for and use of nonopioid pain medications. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Indexed as

Chronic PainOpiate AlkaloidsAcetaminophenAffectAnalgesics, OpioidHumansIbuprofenMotivationAcetaminophenAnalgesics, OpioidIbuprofenOpiate Alkaloids

Identifiers

PMID31556665

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.