Evidence map›Paper›PMID 42027845›Full record

ArticleCrime science2026

Unstructured spare time and crime: toward an integrative model.

David Buil-Gil, Ken Pease

Abstract read
In one paragraph

Article in Crime science, 2026. 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

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.

David Buil-GilDepartment of Criminology, The University of Manchester, Manchester, UK.ORCID 0000-0002-7549-6317
Ken PeaseDepartment of Criminology, The University of Manchester, Manchester, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Criminological theorizing over the past half century has shown little convergence or integration. Three strands of criminological theory can be identified: dispositional approaches (emphasizing self-control, social learning, biological, and morality theories), ecological theories (emphasizing the crime consequences of dysfunctional communities), and opportunity theories (focusing primarily on places and artifacts that enable or facilitate crime). The discipline's progress has not resulted in a convergence of theoretical propositions. This article offers a potential route toward reconciling these approaches, provisionally termed the Unstructured Spare Time model of crime. It begins with an overview of relevant criminological theories and highlights enduring tensions between individual- and opportunity-based approaches. It then reviews previous integrative efforts, noting their contributions and limitations. The Unstructured Spare Time model is introduced as a conceptual bridge among these traditions. The model posits that unstructured spare time, at the level of individuals, geographic areas, and time periods, is shaped by personal factors, broader social changes, and the spatial organization of cities and towns. This unstructured time, in turn, influences both individual readiness for crime and the availability of crime opportunities. The model advances a dynamic view of how time-use patterns mediate the relationship between personal traits, community conditions, structural factors, and exposure to and engagement in crime. Its central contribution lies in focusing explanation and, by extension, prevention and intervention strategies on a single, observable factor: unstructured spare time. The article summarizes empirical support from recent studies and concludes by outlining directions for future research and refinement of the model.

Indexed as

DelinquencyEnvironmental criminologyLeisureOffendingTheoretical criminology

Identifiers

PMID42027845
PMCPMC13099811

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Registered trials

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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.