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Table 3 Unified Validation Framework, strategies to improve validity of mixed-methods research

From: Exploring how nurses assess, monitor and manage acute pain for adult critically ill patients in the emergency department: protocol for a mixed methods study

Component, definition

Strategies

Foundational element, researchers’ knowledge of the phenomenon of interest, methodology

Detailed critique and description of the surrounding literature [1, 7, 83, 84]

Design quality, appropriateness of methods and data analysis techniques in answering the research question

In-depth description and rationale for research design, methods, data analysis and integration choices with reference to the extant literature

Piloting of survey, observation and interview guide to insure accuracy and feasibility (internal validity)

Use of multiple data sources to increase depth of understanding (dependability, reliability)

Use of eligibility criteria and purposive sampling to ensure information-rich participants and observations.

Prolonged engagement with the field/participants to ensure depth of understanding, reduce Hawthorn effect and to build trust (internal validity)

Auditing of transcripts to ensure accuracy (dependability, reliability)

Detailed, thick descriptive data (e.g. direct quotes) to assist reader interpretation and understanding of context (transferability, external validity)

Use of reflective diary to recall decisions made, thoughts, feelings, instincts and challenges (confirmability, objectivity)

Legitimation, collection and integration of quantitative and qualitative data

Participant-driven data collection

Use of and detailed description of complementarity framework in integrating quantitative and qualitative data

Peer review/research team triangulation – coding, interpretation and generation of inferences

Audit trail of decision-making and rationale throughout data collection, analysis and integration processes (dependability, reliability)

Interpretive rigor, whether the meta-inference adequately incorporates inferences stemming from integrated data

Peer review/research team driven generation of meta-inferences

Audit trail of decision-making and rationale in generating of meta-inferences; data used, source(s) and weight within meta-inference

Inferential consistency, relationship between findings and prior understandings, research and theory

Detailed discussion of study findings and relationship to extant literature and theory - highlighting consistencies and discrepancies

Utilisation / Historical element, how integrated data was used

Audit trail of decision-making and rationale in selection of data used

Consequential element, acceptability of findings, or inferences of a study

Peer-reviewed publications

Diverse range of participants

Strengths, limitations and challenges described in detail