The Glasgow ‘Deep End’ Links Worker Study Protocol: a quasi-experimental evaluation of a social prescribing intervention for patients with complex needs in areas of high socioeconomic deprivation

Stewart Mercer, Bridie Fitzpatrick, Lesley Grant, Nai Rui Chng, Catherine A. O’Donnell, Mhairi Mackenzie, Alex McConnachie, Andisheh Bakhshi, Sally Wyke

Research output: Contribution to journalArticle

Abstract

Background
‘Social prescribing’ can be used to link patients with complex needs to local (non-medical) community resources. The ‘Deep End’ Links Worker Programme is being tested in general practices serving deprived populations in Glasgow, Scotland.

Objectives
To assess the implementation and impact of the intervention at patient and practice levels.

Methods
Study design: Quasi-experimental outcome evaluation with embedded theory-driven process evaluation in 15 practices randomized to receive the intervention or not. Complex intervention: Comprising a practice development fund, a practice-based community links practitioner (CLP), and management support. It aims to link patients to local community organizations and enhance practices’ social prescribing capacity. Study population: For intervention practices, staff and adult patients involved in referral to a CLP, and a sample of community organization staff. For comparison practices, all staff and a random sample of adult patients. Sample size: 286 intervention and 484 comparator patients. Outcomes: Primary patient outcome is health-related quality of life (EQ-5D-5L). Secondary patient outcomes include capacity, depression/anxiety, self-esteem, and healthcare utilization. Practice outcome measures include team climate, job satisfaction, morale, and burnout. Outcomes measured at baseline and 9 months. Processes: Barriers and facilitators to implementation of the programme and possible mechanisms through which outcomes are achieved. Analysis plan: For outcome, intention-to-treat analysis with differences between groups tested using mixed-effects regression models. For process, case-study approach with thematic analysis.

Discussion
This evaluation will provide new evidence about the implementation and impact of social prescribing by general practices serving patients with complex needs living in areas of high deprivation.
Keywords: general practice, primary care, multimorbidity, complex interventions, health inequalities
Original languageEnglish
Article number7
Pages (from-to)1-10
JournalJournal of Comorbidity
Volume7
Issue number1
DOIs
Publication statusPublished - 25 Jan 2017
Externally publishedYes

Fingerprint

General Practice
Organizations
Morale
Intention to Treat Analysis
Job Satisfaction
Scotland
Financial Management
Climate
Self Concept
Sample Size
Population
Comorbidity
Primary Health Care
Research Design
Referral and Consultation
Anxiety
Quality of Life
Outcome Assessment (Health Care)
Depression
Delivery of Health Care

Keywords

  • general practice
  • primary care
  • multimorbidity
  • complex interventions
  • health inequalities

Cite this

Mercer, Stewart ; Fitzpatrick, Bridie ; Grant, Lesley ; Chng, Nai Rui ; O’Donnell, Catherine A. ; Mackenzie, Mhairi ; McConnachie, Alex ; Bakhshi, Andisheh ; Wyke, Sally . / The Glasgow ‘Deep End’ Links Worker Study Protocol : a quasi-experimental evaluation of a social prescribing intervention for patients with complex needs in areas of high socioeconomic deprivation. In: Journal of Comorbidity. 2017 ; Vol. 7, No. 1. pp. 1-10.
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The Glasgow ‘Deep End’ Links Worker Study Protocol : a quasi-experimental evaluation of a social prescribing intervention for patients with complex needs in areas of high socioeconomic deprivation. / Mercer, Stewart; Fitzpatrick, Bridie ; Grant, Lesley ; Chng, Nai Rui ; O’Donnell, Catherine A. ; Mackenzie, Mhairi; McConnachie, Alex ; Bakhshi, Andisheh; Wyke, Sally .

In: Journal of Comorbidity, Vol. 7, No. 1, 7, 25.01.2017, p. 1-10.

Research output: Contribution to journalArticle

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AU - Chng, Nai Rui

AU - O’Donnell, Catherine A.

AU - Mackenzie, Mhairi

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AU - Bakhshi, Andisheh

AU - Wyke, Sally

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