Consumer-delivered training in intimate examination skills for healthcare students and professionals

Types of studies

We will include randomised controlled trials (RCTs). We will exclude non-randomised and quasi-randomised trials, as they are associated with a high risk of bias.

Types of participants

Consumers and healthcare students or professionals.

Types of interventions

Historically, consumers have played a fundamental role in medical education as passive, clinical exemplars (Jha 2010). More recently, this role has developed and consumers have become actively involved in training healthcare professionals, that is, consumer-delivered training (Jha 2010). We will include trials that compare consumer-delivered training with any other single or combined training intervention including didactic sessions, audio-visual demonstrations, instruction involving mannequins, or instruction within clinical settings involving anaesthetised or conscious patients.

Types of outcome measures

Electronic searches

We will search the following electronic databases:

1. the Cochrane Central Register of Controlled Trials (CENTRAL);

2. MEDLINE;

3. EMBASE;

4. PsycINFO.

We present the strategy for EMBASE Ovid SP in Appendix 1. We will tailor strategies the other databases and report them in the full review.

We will include other electronic searches for trials:

1. trial registers for ongoing and registered trials: ClinicalTrials.gov (clinicaltrials.gov/ct2/home), and the World Health Organization International Clinical Trials Registry Platform (www.who.int/trialsearch/Default.aspx);

2. citation indexes: Social Sciences Citation Index (scientific.thomson.com/products/sci);

3. conference abstracts in the Web of Knowledge (wokinfo.com);

4. LILACS database, for trials from the Portuguese and Spanish-speaking world;

5. PubMed;

6. OpenGrey database and Google for grey literature.

Searching other resources

We will handsearch reference lists of articles retrieved by the search and contact experts in the field to obtain additional data.

Selection of studies

Two review authors will independently screen all titles and abstracts identified from searches to determine those that meet the inclusion criteria. We will retrieve in full text any papers identified as potentially relevant by at least one review author. Two review authors will independently screen full-text articles for inclusion or exclusion, and resolve discrepancies by discussion and, if necessary, consultation with a third review author to reach consensus. We will list as excluded studies all potentially relevant papers excluded from the review at this stage, with reasons provided in the 'Characteristics of excluded studies' table. We will also provide citation details and any available information about ongoing studies, and collate and report details of duplicate publications, so that each study (rather than each report) is the unit of interest in the review. We will report the screening and selection process in an adapted PRISMA flow chart (Liberati 2009).

Data extraction and management

Two review authors will extract data independently from included studies. We will resolve any discrepancies by discussion until consensus is reached. We will develop and pilot a data extraction form using the Cochrane Consumers and Communication Review Group Data Extraction Template (cccrg.cochrane.org/author-resources). Data extracted will include study characteristics and outcome data. Where studies have multiple publications, we will use the main trial report as the reference, with additional details derived from the secondary papers. We will correspond with study investigators for further data on methods or results (or both), as required. One review author will enter all extracted data into Review Manager 5 (RevMan 2012), and a second review author will independently check extracted data for accuracy against the data extraction sheets.

Assessment of risk of bias in included studies

We will assess and report on the methodological risk of bias of included studies in accordance with the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2011), and the guidelines of the Cochrane Consumers and Communication Review Group (Ryan 2014), which recommends the explicit reporting of the following individual elements for RCTs: random sequence generation; allocation sequence concealment; blinding (participants, personnel); blinding (outcome assessment); completeness of outcome datal; selective outcome reporting; and other sources of bias. We will consider blinding separately for different outcomes where appropriate (e.g. blinding may have the potential to affect subjective versus objective outcome measures differently). We will judge each item as being at high, low, or unclear risk of bias as set out in the criteria provided by the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2011), and provide a quote from the study report and a justification for our judgement for each item in the 'Risk of bias' table. We will deem studies to be at the highest risk of bias if they are scored as at high or unclear risk of bias for either the sequence generation or allocation concealment domains, based on growing empirical evidence that these factors are particularly important potential sources of bias (Higgins 2011). In all cases, two review authors will independently assess the risk of bias of included studies, and will resolve any disagreements by discussion to reach consensus. We will contact study authors for additional information about the included studies, or for clarification of the study methods as required. We will incorporate the results of the risk of bias assessment into the review through standard tables, and systematic narrative description and commentary about each of the elements, leading to an overall assessment of the risk of bias of included studies and a judgement about the internal validity of the review's results.

Measures of treatment effect

For dichotomous outcomes, we will analyse data based on the number of events and the number of people assessed in the intervention and comparison groups. We will use these to calculate the risk ratio (RR) and 95% confidence interval (CI). For continuous measures, we will analyse data based on the mean, standard deviation (SD) and number of people assessed for both the intervention and comparison groups to calculate mean difference (MD) and 95% CI. If the MD is reported without individual group data, we will use this to report the study results. If more than one study measures the same outcome using different tools, we will calculate the standardised mean difference (SMD) and 95% CI using the inverse variance method in Review Manager 5 (RevMan 2012).

Unit of analysis issues

The primary analysis will be per healthcare student or professional randomised. We will briefly summarise data that do not allow valid analysis and will not include them in the meta-analysis.

Dealing with missing data

We will attempt to contact study authors to obtain missing data (participant, outcome, or summary data). For participant data, we will, where possible, conduct analysis on an intention-to-treat basis; otherwise, we will analyse data as reported. We will report on the levels of loss to follow-up and assess this as a source of potential bias.

Assessment of heterogeneity

Where we consider studies similar enough to allow pooling of data using meta-analysis, we will assess the degree of heterogeneity by visual inspection of forest plots and by examining the Chi² test for heterogeneity. We will quantify heterogeneity using the I² statistic. We will consider an I² value of 50% or more to represent substantial levels of heterogeneity, but we will interpret this value after considering the size and direction of effects and the strength of the evidence for heterogeneity, based on the P value from the Chi² test (Higgins 2011).

Where we detect substantial clinical, methodological, or statistical heterogeneity across included studies, we will not report pooled results from meta-analysis but will instead use a narrative approach to data synthesis. In this event, we will attempt to explore possible clinical or methodological reasons for this variation by grouping studies to explore differences in intervention effects.

Note that when there are few trials in a meta-analysis, the Chi² test has little power to detect heterogeneity. Therefore, a non-significant result should not necessarily be interpreted as evidence of no heterogeneity and should instead be interpreted with care.

Assessment of reporting biases

We will assess reporting bias qualitatively based on the characteristics of the included studies (e.g. if only small studies that indicate positive findings are identified for inclusion), and if information that we obtain from contacting experts and authors or studies suggests that there are relevant unpublished studies. If we identify sufficient studies (at least 10) for inclusion in the review, we will construct a funnel plot to investigate small-study effects, which may indicate the presence of publication bias. We will formally test for funnel plot asymmetry, with the choice of test made based on advice in the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2011), and considering that there may be several reasons for funnel plot asymmetry when interpreting the results.

Data synthesis

We will decide whether to meta-analyse data based on whether the interventions in the included trials are similar enough in terms of participants, settings, intervention, comparison, and outcome measures to ensure meaningful conclusions from a statistically pooled result. Due to the anticipated variability in the interventions of included studies, we will use a random-effects model for meta-analysis.

If we are unable to pool the data statistically using meta-analysis, we will group the data based on the category that best explores the heterogeneity of studies and makes most sense to the reader (i.e. by interventions, populations, or outcomes). Within each category, we will present the data in tables and narratively summarise the results.

Subgroup analysis and investigation of heterogeneity

Where data are available, we will conduct subgroup analyses to determine the separate evidence within the following subgroups:

1. learner group (healthcare student, healthcare professional), as the intervention's effects may vary with different populations of healthcare students or professionals;

2. consumer-delivered intimate examination training delivered as a stand-alone component or part of other training techniques, as the intervention's effects may vary according to the effects of other training received.

If we detect substantial heterogeneity, we plan to explore possible explanations in sensitivity analyses. We plan to take any statistical heterogeneity into account when interpreting the results, especially when there is any variation in the direction of effect.

Sensitivity analysis

We will conduct a sensitivity analyses for the primary outcome to determine whether the conclusions would have differed if:

1. we restrict eligibility to studies at low risk of bias: if we rate an included study at low risk of bias for sequence generation and allocation concealment then we will consider it at low risk of bias for the purposes of the sensitivity analysis;

2. we adopted a fixed-effect model.