Evidence map›Paper›PMID 34147105›Full record

ArticleHealth research policy and systems2021

Understanding the complexity of population health interventions: assessing intervention system theory (ISyT).

Linda Cambon, François Alla

Abstract readLetter
In one paragraph

Article in Health research policy and systems, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 1 pooled it
–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

14 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Review
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
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

2 authors.

Linda CambonChaire de Prévention ISPED/SPF, Université de Bordeaux, Bordeaux, France. linda.cambon@u-bordeaux.fr.ORCID http://orcid.org/0000-0001-6040-9826
François AllaChaire de Prévention ISPED/SPF, Université de Bordeaux, Bordeaux, France.ORCID http://orcid.org/0000-0002-5793-7190

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Given their inherent complexity, we need a better understanding of what is happening inside the "black box" of population health interventions. The theory-driven intervention/evaluation paradigm is one approach to addressing this question. However, barriers related to semantic or practical issues stand in the way of its complete integration into evaluation designs. In this paper, we attempt to clarify how various theories, models and frameworks can contribute to developing a context-dependent theory, helping us to understand the black box of population health interventions and to acknowledge their complexity. To achieve this goal, we clarify what could be referred to as "theory" in the theory-driven evaluation of the interventional system, distinguishing it from other models, frameworks and classical theories. In order to evaluate the interventional system with a theory-driven paradigm, we put forward the concept of interventional system theory (ISyT), which combines a causal theory and an action model. We suggest that an ISyT could guide evaluation processes, whatever evaluation design is applied, and illustrate this alternative method through different examples of studies. We believe that such a clarification can help to promote the use of theories in complex intervention evaluations, and to identify ways of considering the transferability and scalability of interventions.

Indexed as

Population HealthHumansResearch DesignSystems TheoryEvaluationPublic healthSystemTheory

Identifiers

PMID34147105
PMCPMC8214800

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.