Health policies in long-term care facilities for older adults: A systematic review of textual evidence

TL;DR

This paper outlines a method to systematically review health policies in long-term care facilities for older adults to improve care quality and governance.

Contribution

It introduces a novel protocol using the JBI framework to synthesize textual health policy evidence in LTC settings.

Findings

The protocol uses the JBI framework for a systematic review of textual health policies in LTC.

It includes a three-step search strategy across MEDLINE, CINAHL, and BVS for policy documents.

The review will highlight policy characteristics, implementation strategies, and outcomes.

Abstract

Health polices in residential care settings for older adults are essential to align housing and care provision with the needs of ageing populations, ensuring safety, participation, and quality of life. Despite their importance, evidence regarding policy implementation in long-term care (LTC) facilities remains fragmented and inconsistent.

This protocol describes an original method for synthesizing health policy documents using the Joanna Briggs Institute (JBI) framework for textual evidence. It aims to identify, analyze, and integrate policy evidence related to LTC facilities for older adults.

Following JBI methodological guidance for systematic reviews of text and opinion, this protocol employs the PICo framework to define inclusion criteria and a three-step search strategy. Searches will be conducted in MEDLINE, CINAHL Complete®, and the Virtual Health Library (BVS). Eligible documents include laws, regulations, policy frameworks, and technical guidelines addressing long-term care within publicly regulated systems. Data extraction and quality appraisal will be independently performed by two reviewers using JBI instruments.

The review will synthesize existing polices, highlighting their characteristics, implementation strategies, and outcomes. By applying a transparent and replicable JBI-based method, this protocol supports the production of high-quality evidence to inform equitable and effective governance in LTC facilities

The image included in the graphical abstract was generated with OpenAI’s ChatGPT from an author-written prompt; the content was reviewed by the authors; no real individuals are depicted.

Image, graphical abstract

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