Home » Perceived guideline clarity influences guideline-concordant breast cancer screening care in women aged 40–49 | BMC Women’s Health

Perceived guideline clarity influences guideline-concordant breast cancer screening care in women aged 40–49 | BMC Women’s Health

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Perceived guideline clarity influences guideline-concordant breast cancer screening care in women aged 40–49 | BMC Women’s Health

Study design, data measurement and data collection

The overall study design and data collection methods have been described previously and followed by the COREQ checklist (Supplementary Material) [13]. Briefly, individual semi-structured telephone interviews were conducted with a sample of primary care physicians in the Greater Toronto Area, Canada between January and November 2020. Interviews were de-identified and professionally transcribed. The interview guide and initial analysis of barriers and facilitators were structured and informed by the theoretical domains framework (TDF). The TDF is a theoretically informed, comprehensive determinants framework used to examine the underlying determinants (ie, barriers/facilitators) of a specific behavior(s) [15, 16]. Ethics approval was obtained at Women’s College Hospital # 2019-0141-E.

During the interviews, it became clear that asking about “directions” was confusing for the participants; therefore, the interview guide was modified to ask about routine practice related to mammography in this age group, and this was followed by TDF-based questions about their practice and behaviors of interest. After this part of the discussion was completed, the specific wording of the key recommendation of the Canadian Task Force on Breast Screening Guidelines for Ages 40-49 was read to the participants and their reactions and opinions were captured. The interviewer (MBN) identified himself as a breast cancer medical oncologist. Her reflexivity was to remain as neutral as possible with a view to understanding each participant’s clinical practice, experience and reasons for doing so. She normalizes any questions or concerns of the participants in a neutral tone, i.e. stating that in her clinical experience she has seen several different approaches to screening, that there is variation among participants’ responses, there are no “wrong” answers. MBN said the purpose of the study was to try to understand the reasons behind each doctor’s clinical practice. The second coding author (AMC) is the project leader and a qualitative researcher with a background in women’s health. The project team is experienced in screening data and implementing guidelines. When both coders did not find any new information relevant to the research question in 2–3 consecutive transcripts, the team looked for nonconfirming cases by interviewing physicians in underrepresented demographic categories (men and lower-resource areas). After 3 additional interviews with these participants, the team concluded that saturation had been reached as themes continued to accurately explain screening behavior and drivers of variation.

Data analysis

First, we created an operational definition of guideline-consistent mammography screening in this age group, including (i) specific assessment of breast cancer risk and (ii) informed discussion of the benefits and harms of screening mammography. If the doctor decides that screening is appropriate (the benefits probably outweigh the harms), but the patient chooses not to be screened, this is considered guideline compliance, as patients have the right to refuse testing. If the doctor thinks that screening is inappropriate (the harms probably outweigh the benefits), the doctor should recommend screening; however, if such a patient expresses a desire for screening, the referral may still be considered guideline-compliant after eliciting the patient’s values, discussing benefits and harms, and obtaining informed consent.

A pragmatic, focused content analysis approach was taken in which data were deductively coded to relevant TDF domains representing barriers and facilitators of screening behavior [17, 18]. Interview transcripts were coded independently by two members of the research team (MBN & AMC), coding first by behavior of interest and then by identifying and applying the relevant TDF code. If researchers felt that a piece of transcript was relevant to the research question or changed screening behavior but did not fit into a TDF domain, it was inductively coded. In the initial analysis, individual behavior was assessed for the presence of specific TDF-barriers and facilitators to understand the influences on each screening behavior. For this analysis, the research team sought to identify common themes that act as contextual factors (causes) or consequences of variation in screening behaviors. The research team met repeatedly to review all inductive codes, which included definitions and a list of sample quotes. The list of inductive codes was discussed, compared with potential strong cases or variations, and narrowed until they either applied to more than one screening behavior as either an overarching driver of the behavior or helped to explain the influence or consequences of some of the TDF domains .

For our secondary objective, a framework approach was used to examine whether demographic characteristics explained sources of variation in the third mammography decision and referral behavior data set [19]. A priori demographic variables and TDF domains for analysis were informed by the literature and previous work. Demographic variables included provider gender, practice location (urban vs. suburban), Canadian vs. non-Canadian medical graduate, and age (above or below average). For practice location, providers from Toronto, Thornhill, and North York were grouped together as resource-rich because these providers described their practices as being located in or adjacent to a high-resource environment in Toronto, Ontario. Providers from Scarborough, Brampton, Pickering, Ajax, and Orangeville were categorized as lower-resource environments because these areas were further from Toronto and providers described access to resources in Toronto but a lack of similar resources locally (and /or their patients had increased barriers to accessing Toronto-based resources). The TDF domains selected for the framework analysis included Social Influence – Radiologists, Social Influence – Patient, Emotion, Ability Beliefs, and Consequence Beliefs because they were the most common domains influencing the third screening behavior in a subset of participants . Our initial analysis found that the TDF code “social influence” could be applied by both patients and radiologists, so these were coded as separate barriers for the coding and analyses. The presence or absence of the determinants of interest were categorized in the transcripts. Only determinants that had the same direction of association (eg, the social influence of the radiologist had to be that this influence favors screening behavior) were counted. When the expression of the determinant is more strongly present for one demographic group compared to another (eg, males vs. females), the difference is reported.

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