The gig economy is a promising arena to reduce unemployment and provide other benefits such as the opportunity to earn supplemental income. Like all other forms of work, the gig space also presents occupational health issues for those working in it. This proposed review is aimed at identifying and describing the common occupational health outcomes reported within this workforce; second, to examine the risk factors that contribute to the development of these health issues; and third, to assess the interventions and support systems currently in place to promote the occupational health of gig workers.

A systematic review will be undertaken according to the recommendations of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Statement (2009). A search from 2015 to 2025 will be conducted on four global databases (Web of Science, SCOPUS, Academic Source Complete and Business Source Complete). Only records in English, full text and peer-reviewed journal articles will be included. Book chapters, thesis, reports and systematic reviews will be excluded. The Joanna Briggs Institute Critical Appraisal Tools will be used to assess the methodological rigour of various studies prior to inclusion for the final analysis. The extracted data will be synthesised using a narrative synthesis approach, integrating findings from both quantitative and qualitative studies.

This research is exempt from ethics approval because the work will be carried out on published documents. We will disseminate this protocol in a related peer-reviewed journal.

CRD420250654059.

To integrate the quantitative and qualitative data collected as part of the PEACH (Procalcitonin: Evaluation of Antibiotic use in COVID-19 Hospitalised patients) study, which evaluated whether procalcitonin (PCT) testing should be used to guide antibiotic prescribing and safely reduce antibiotic use among patients admitted to acute UK National Health Service (NHS) hospitals.

Triangulation to integrate quantitative and qualitative data.

Four data sources in 148 NHS hospitals in England and Wales including data from 6089 patients.

A triangulation protocol was used to integrate three quantitative data sources (survey, organisation-level data and patient-level data: data sources 1, 2 and 3) and one qualitative data source (clinician interviews: data source 4) collected as part of the PEACH study. Analysis of data sources initially took place independently, and then, key findings for each data source were added to a matrix. A series of interactive discussion meetings took place with quantitative, qualitative and clinical researchers, together with patient and public involvement (PPI) representatives, to group the key findings and produce seven statements relating to the study objectives. Each statement and the key findings related to that statement were considered alongside an assessment of whether there was agreement, partial agreement, dissonance or silence across all four data sources (convergence coding). The matrix was then interpreted to produce a narrative for each statement.

To explore whether PCT testing safely reduced antibiotic use during the first wave of the COVID-19 pandemic.

Seven statements were produced relating to the PEACH study objective. There was agreement across all four data sources for our first key statement, ‘During the first wave of the pandemic (01/02/2020-30/06/2020), PCT testing reduced antibiotic prescribing’. The second statement was related to this key statement, ‘During the first wave of the pandemic (01/02/2020-30/06/2020), PCT testing safely reduced antibiotic prescribing’. Partial agreement was found between data sources 3 (quantitative patient-level data) and 4 (qualitative clinician interviews). There were no data regarding safety from data sources 1 or 2 (quantitative survey and organisational-level data) to contribute to this statement. For statements three and four, ‘PCT was not used as a central factor influencing antibiotic prescribing’, and ‘PCT testing reduced antibiotic prescribing in the emergency department (ED)/acute medical unit (AMU),’ there was agreement between data source 2 (organisational-level data) and data source 4 (interviews with clinicians). The remaining two data sources (survey and patient-level data) contributed no data on this statement. For statement five, ‘PCT testing reduced antibiotic prescribing in the intensive care unit (ICU)’, there was disagreement between data sources 2 and 3 (organisational-level data and patient-level data) and data source 4 (clinician interviews). Data source 1 (survey) did not provide data on this statement. We therefore assigned dissonance to this statement. For statement six, ‘There were many barriers to implementing PCT testing during the first wave of COVID-19’, there was partial agreement between data source 1 (survey) and data source 4 (clinician interviews) and no data provided by the two remaining data sources (organisational-level data and patient-level data). For statement seven, ‘Local PCT guidelines/protocols were perceived to be valuable’, only data source 4 (clinician interviews) provided data. The clinicians expressed that guidelines were valuable, but as there was no data from the other three data sources, we assigned silence to this statement.

There was agreement between all four data sources on our key finding ‘during the first wave of the pandemic (01/02/2020-30/06/2020), PCT testing reduced antibiotic prescribing’. Data, methodological and investigator triangulation, and a transparent triangulation protocol give validity to this finding.

ISRCTN66682918.