Evidence map›Paper›PMID 39171302›Full record

ArticleFrontiers in public health2024

Taking the Big Leap | understanding, accessing and improving behavioural science interventions.

Nishan Gantayat, Anushka Ashok, Pallavi Manchi, Rosemary Pierce-Messick, Rahul Porwal, Alok Gangaramany

Abstract read
In one paragraph

Article in Frontiers in public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Nishan GantayatFinal Mile Consulting, New York, NY, United States.
Anushka AshokFinal Mile Consulting, New York, NY, United States.
Pallavi ManchiFinal Mile Consulting, New York, NY, United States.
Rosemary Pierce-MessickFinal Mile Consulting, New York, NY, United States.
Rahul PorwalFinal Mile Consulting, New York, NY, United States.
Alok GangaramanyFinal Mile Consulting, New York, NY, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Applied behaviour science's focus on individual-level behaviours has led to overestimation of and reliance on biases and heuristics in understanding behaviour and behaviour change. Behaviour-change interventions experience difficulties such as effect sizes, validity, scale-up, and long-term sustainability. One such area where we need to re-examine underlying assumptions for behavioural interventions in Human Immunodeficiency Virus (HIV) and Tuberculosis (TB) prevention, which seek population-level benefits and sustained, measurable impact. This requires taking a "Big Leap." In our view, taking the big leap refers to using a behavioural science-informed approach to overcome the chasms due to misaligned assumptions, tunnel focus, and overweighting immediate benefits, which can limit the effectiveness and efficiency of public health programmes and interventions. Crossing these chasms means that decision-makers should develop a system of interventions, promote end-user agency, build choice infrastructure, embrace heterogeneity, recognise social and temporal dynamics, and champion sustainability. Taking the big leap toward a more holistic approach means that policymakers, programme planners, and funding bodies should "Ask" pertinent questions to evaluate interventions to ensure they are well informed and designed.

Indexed as

Behavioral SciencesHIV InfectionsHumansPublic HealthTuberculosisbehavioural economicsbehavioural scienceHIV preventionpolicypublic healthsustainabilityTB

Identifiers

PMID39171302
PMCPMC11335669

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.