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Atlas.ti 8

Manufactured by ATLAS.ti
Sourced in Germany

Atlas.ti 8 is a software application designed for qualitative and mixed-method research. It provides tools for organizing, analyzing, and visualizing data from various sources, including interviews, focus groups, and textual documents. The software's core function is to assist researchers in managing and exploring their research data.

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35 protocols using Atlas.ti 8

Interview data were deidentified, analyzed, and coded independently by 2 members of the research team (K.J.B. and M.Y.). An inductive coding method was used to identify and organize the data into general themes. Four transcripts were initially transcribed to establish a coding scheme, after which investigators discussed discrepancies and finalized the coding scheme. Discrepancies were adjudicated by a third reviewer (S.L.). An additional 20 transcripts were coded to further test and refine the categories. A total of 85 codes were used to describe themes present in 755 quotes. Frequency of themes were analyzed by type of facility and whether formal programming was present for SUD/OUD residents. Coding and qualitative analyses were completed using ATLAS.ti 8.4.4 (ATLAS.ti Scientific Software Development GmbH, Berlin, Germany).
Sociodemographic interviewee characteristics, facility payor distribution, and Likert-type responses were analyzed using a Mann-Whitney U test given a small sample size and a nonnormal distribution. Statistical analyses were conducted in Stata version 16 (StataCorp LLC, College Station, TX).
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Content analysis was done by applying a general inductive approach using ATLAS.ti (8.4.4) software.22 To ensure reliability, all transcripts were coded independently by the main researcher (R.B.) and a second member of the research team (E.B., E.G., J.v.W., A.R. or F.L.). Differences were discussed until consensus was reached, where about one-fifth of all codes were modified. Coders used an open, bottom-up, inductive coding style. Next, overarching categories were identified by one researcher, and checked by a second. In order to increase validity, multiple members of the research team identified the final categories. Analysis remained at a category level, in order to not lose valuable information by summarising at a theme level.
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Using IBM SPSS Statistics 24.0 (IBM Corporation, New York) for quantitative data analysis, we computed means, standard deviations, and percentages. The 4-point Likert scale items were dichotomized to ‘strong disagree/disagree’ versus ‘strongly agree/agree’ due to bimodal distributions observed of the items. For open-ended qualitative items, we used Atlas.ti 8.0 (ATLAS.ti GmbH, Germany) to organize and code the data. Author team members independently reviewed the qualitative responses, developed initial codes, and formulated candidate themes. Through iterative group reviews and discussions, we refined and generated a final set of themes and subthemes. We integrated quantitative findings with emergent qualitative themes. For leadership attainment, frequency counts and percentages were computed for the entire sample of 2005–2012, and by the following graduate categories of focus: women faculty, URM, and women URM faculty. URM was defined as ethnicities and racial groups considered to be URMs in health sciences, which included African American/Black, Filipino or Hmong or Vietnamese Asians, Hispanic/Latino, Native American/Alaskan Native, or Native Hawaiian/Other Pacific Islander, or two or more races with one or more from the URM categories [17].
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We anonymised all recorded notes and audio. We chose thematic analysis for obtaining a systematic framework to code qualitative data to identify patterns across the data [32 ]. The transcripts were transcribed in the local language (Hindi). Further, the recorded transcripts were translated into English to understand the theme by the wider population. KK coded transcripts and organised them by the sub-group of the participants. We used qualitative data software Atlas-ti 8.0 for the thematic analysis of data. We used direct quotes for exemplary purposes. The senior author (NS) reviewed typed transcripts for accuracy, completion, and plausibility. We also looked for the data saturation to validate our findings.
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Qualitative data was analyzed using grounded theory 47 and thematic analysis.48 We employed solo coding by following Saldaña’s 48 recommendations that required consulting with another trained researcher during the progress of the data analysis to discuss, process, and validate the findings. In addition, the coder also kept memos during data analysis. First the trained researcher began with open or initial coding of transcripts to generate a list of codes. Codes were analyzed and facilitated creating categories and sub-categories based on patterns from the data. The relationships between categories and sub-categories were assessed and established themes related to perceived experiences of discrimination in the workplace and the operationalization of people-oriented culture. Data was analyzed using Atlas.ti 8.0 which is a software that facilitates the organization and analysis of text, images, audio, and video data.
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Once transcribed and made anonymous, the interviews were analyzed following the steps of Giorgi’s phenomenological method. First, the interview transcription was read and re-read to identify significant information in response to the research questions and study objectives. Once data were highlighted, significant units were identified and coded according to the suggested meaning determined by the analyst researcher. Codes were all defined in a glossary, which allowed an initial interpretation. These codes were regrouped, generating themes and subthemes. Grouping was based on the shared meaning of codes and facilitated by comparing attitude and relationship linking (through network diagramming). Finally, in an interpretative exercise aiming to integrate all themes and subthemes, a central theme emerged, which encapsulates the essence of the experience [28 (link)].
Two investigators independently conducted the data analysis, reaching a consensus about the results afterward. In case of disagreement, a third investigator was accessed. During the data analysis, the software Atlas-ti 8.0 was utilized as an aid.
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For data analysis, the transcriptions were included in a hermeneutic unit and analysed using the Atlas-ti 8.0. software. To ensure the reliability and validity of the results, the researchers followed the Colaizzi [29 (link)] methods of descriptive phenomenological data analysis: (1) Completed transcript of the interview and understood the participants´ lived experiences; (2) Significant sentences were scrutinized to meaningful statements; (3) Meaningful statements were extracted to meaningful units; (4) Meaningful units were classified into subthemes and themes; (5) Themes and subthemes were integrated into a comprehensive description of the participants´ lived experiences; (6) The basic structure of the participants´ lived experiences was described; (7) Two interviewees analysed the findings for verification of the accuracy of the transcripts and resemblance of their experiences.
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Descriptive analyses of quantitative data (nurse characteristics and tasks and responsibilities) were performed using IBM SPSS statistics version 22.0. For qualitative analyses (perception on physical activity promotion, tasks and responsibilities and most important factors), a deductive approach (Elo & Kyngas, 2008) with directed content analysis was used (Hsieh & Shannon, 2005).
The interviews were fully transcribed. Initial codes and categories were based on the main topics of the interview design. Interview transcripts were read through, and initial coding was assigned to the correlating text. The categorization matric with pre‐determined categories and codes is presented in Table S1. Subsequently, open coding was used to determine possible new codes and categories for data not corresponding with the pre‐determined codes. Atlas.ti 8.0 was used in the qualitative coding process. Discussions on interpreting data took place between two researchers, and all codes and data were verified by a second researcher.
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We conducted in-person qualitative interviews (n=11) with a subset of trial participants in the intervention arm (4 Spanish, 4 English and 3 Chinese). Written informed consent was obtained immediately prior to each interview, and participants received taxi vouchers and a $25 gift card. Interview questions addressed participants’ health research information-seeking, perceptions of the HREI, and relationships with SCN. Interviews were audio recorded and transcribed/translated verbatim. Coding and data analysis were conducted using Atlas-ti 8.0. Two members of the team read and coded the first few transcripts independently and then met to reconcile coding discrepancies and establish a codebook. They coded the remaining transcripts using the codebook, meeting to discuss and reconcile any discrepancies. Coded text was subsequently re-read by the coders who wrote memos to describe emerging themes. These memos were then discussed at monthly team meetings where investigators finalized the themes described below.
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Qualitative analysis is a non-linear, interactive and dynamic process. It will consist of different parts following Colaizzi’s 7-stage method [43 (link)], which is based on: 1 –Reading and rereading the transcript; 2 –Extracting significant statements that pertain to the phenomenon; 3 –Formulating meanings from significant statements; 4 –Aggregating formulated meanings into theme clusters and themes; 5 –Developing an exhaustive description of the phenomenon’s essential structure or essence; 6 –Generating a description of the fundamental structure of the phenomenon; and 7 –Validating the findings of the study through participant feedback. The analysis will be performed using the program Atlas.ti 8.0.
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