Evidence map›Paper›PMID 25194784›Full record

ArticleJournal of affective disorders2014

Predictors of patient non-attendance at Improving Access to Psychological Therapy services demonstration sites.

Laura Di Bona, David Saxon, Michael Barkham, Kim Dent-Brown, Glenys Parry

Open access · hybridAbstract read
In one paragraph

Article in Journal of affective disorders, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
4.4field-weighted citation impact, top 6% of its field
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

9 citing papers in PubMed, 37 citations in OpenAlex.

  1. Trial
  2. Anxiety and depression in emerging adults: The STAND program as a model of scalable screening and intervention.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2026
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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

5 authors at 2 institutions in 1 country.

Laura Di BonaSchool of Health and Related Research, University of Sheffield, Regent Court, 30 Regent Street, South Yorkshire, Sheffield S1 4DA, UK. Electronic address: l.dibona@sheffield.ac.uk.
David SaxonSchool of Health and Related Research, University of Sheffield, Regent Court, 30 Regent Street, South Yorkshire, Sheffield S1 4DA, UK.
Michael BarkhamCentre for Psychological Services Research, University of Sheffield, Sheffield S1 4DA, UK.
Kim Dent-BrownDepartment of Psychology, University of Hull, Hull HU6 7RX. Previously, School of Health and Related Research, University of Sheffield, Sheffield S1 4DA.
Glenys ParrySchool of Health and Related Research, University of Sheffield, Regent Court, 30 Regent Street, South Yorkshire, Sheffield S1 4DA, UK; Centre for Psychological Services Research, University of Sheffield, Sheffield S1 4DA, UK.
University of Sheffield · GBUniversity of Hull · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundImproving Access to Psychological Therapy (IAPT) services have increased the number of people with common mental health disorders receiving psychological therapy in England, but concerns remain about how equitably these services are accessed.

methodUsing cohort patient data (N=363) collected as part of the independent evaluation of the two demonstration sites, logistic regression was utilised to identify socio-demographic, clinical and service factors predictive of IAPT non-attendance.

resultsSignificant predictors of IAPT first session non-attendance by patients were: lower non-risk score on the Clinical Outcomes in Routine Evaluation-Outcome Measure (CORE-OM); more frequent thoughts of "being better off dead" (derived from the CORE-OM); either a very recent onset of common mental health disorder (1 month or less) or a long term condition (more than 2 years); and site. LIMITATIONS: The small sample and low response rate are limitations, as the sample may not be representative of all those referred to IAPT services. The predictive power of the logistic regression model is limited and suggests other variables not available in the dataset may also be important predictors.

conclusionsThe clinical characteristics of risk to self, severity of emotional distress, and illness duration, along with site, were more predictive of IAPT non-attendance than socio-demographic characteristics. Further testing of the relationship between these variables and IAPT non-attendance is recommended. Clinicians should monitor IAPT uptake in those they refer and implement strategies to increase their engagement with services, particularly when referring people presenting with suicidal ideation or more chronic illness.

Indexed as

PsychotherapyAdultEnglandFemaleHealth Knowledge, Attitudes, PracticeHealth Services AccessibilityHumansLogistic ModelsMaleMental DisordersMiddle AgedOutcome Assessment, Health CarePatient ComplianceAccessImproving Access to Psychological Therapy (IAPT) servicesNon-attendancePersonal characteristicsPsychotherapyRisk

Identifiers

PMID25194784
PMCPMC4194348
OpenAlexW2054854200

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-SA
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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.