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Manufactured by ATLAS.ti
Sourced in Germany, United States

ATLAS.ti is a qualitative data analysis software that provides a comprehensive set of tools for organizing, analyzing, and visualizing textual, audio, and visual data. It offers features for coding, annotating, and grouping data, as well as for creating networks and exploring relationships within the data. ATLAS.ti is designed to support researchers, analysts, and professionals working with qualitative data in various fields.

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35 protocols using software

1

Thematic Analysis of Interview Transcripts

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The content of the interviews will be independently analyzed by 2–3 members of the research team, using thematic analysis [39 (link)]. The codes are compared in joint sessions to seek consensus. Thematic analysis first summarizes the transcribed original text by dividing the text into small units (paragraphs, sentences or phrases) that will then be categorized. The categories will describe the content of the interviews. During the analysis, members of the research team will be blinded to individual data of participants in the interviews and focus groups. The research team will discuss the categories until they reach consensus. Primary subcodes will be combined to superior codes and these will be grouped to themes. Data will be analyzed using ATLAS.ti software (ATLAS.ti Scientific Software Development GmbH, Germany).
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2

Qualitative Exploration of Experiences

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All interviews were recorded, transcribed, translated into English and back-translated (Brislin, 1970 ). Key findings from interviews were discussed between interviewers and investigators to understand broad themes. Transcripts were reviewed and thematically coded (BAG) and then collectively reviewed and discussed (BAG, SB, RR, FLA) to understand the prominent themes that emerged. After finalising coding structure, interviews were reviewed again to ensure accuracy of all codes. Coding and analysis was done using Atlas.ti software (ATLAS.ti, 2014). Institutional Review Boards at Yale University and the University of Malaya approved the study.
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3

In-Depth Interviews on HPV Screening

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On the day of the in-depth interview, the interviewer reviewed the informed consent documents with participants and, upon obtaining consent, began the audio-recorded, in-depth interview. The interviewer used the in-depth interview guide to steer the conversation, varying the questions and probes based on participant responses and the direction of the conversation.20 Upon completing the interview, the research team member provided the participant with a monetary incentive ($60) and conducted a short health education lesson about HPV infection, anal dysplasia screening, and anal cancer.
All digitally recorded interviews were transcribed by a local transcription company. The researchers created an initial codebook of a priori themes based on the in-depth interview guide to direct their qualitative content analysis.21 (link),22 The team then coded a single, in-depth interview together to ensure that the information was similarly coded. Three additional transcripts were coded separately and the team then met to compare codes, resolve any coding discrepancies, and discuss the codebook.23 The researchers then coded an additional interview together to test the updated codebook. The team then split the remaining transcripts and separately hand-coded the interviews and organized all coded data on Atlas.ti software (ATLAS.ti, Berlin, Germany).
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4

Developing Robust Coding Protocols

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An initial code guide was developed by two of the researchers (NB and AM), and expanded to incorporate emergent ideas from an initial selection of transcripts. Following multiple readings of all transcripts, a final version of the code guide was developed through consensus by NB and AM. The final code guide allowed us to capture all major ideas raised by the participants in each thematic category. Transcript coding was done in Atlas.ti software (version 7.5.12) by three researchers (NB, AM and SA). To enhance inter-coder reliability, the researchers independently applied the code guide to a selected transcript, and then reviewed and resolved any differences. Reports of quotations for selected codes were generated in Atlas.ti software, and the researchers prepared content summaries of findings.
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5

Evaluating a Digital Health Intervention

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Survey data were transferred from RedCap to STATA and Tableau for data analysis and visualization. Descriptive statistics (e.g. means, standard errors) were generated for all study variables. Weekly averages for app use, feature feedback, and tech acceptance were calculated, and used to generate a total weekly measure for each category. ANOVA analysis was conducted to compare averages in app use, feature feedback, and tech acceptance between each week. Statistical significance was set a p<0.05, however these values are solely provided for reference as our study was not adequately powered to detect significance.
Qualitative interview data was analyzed using Atlas.ti™ software. A qualitative descriptive approach was used, and transcripts were coded using open-coding methods followed by axial coding in which patterns amongst codes were identified, and codes were subsequently categorized into broader themes. Repetitive themes were used to reflect participant perspectives of the overall SavvyHER intervention coupled with perspectives regarding the specific components of the SavvyHER app [18 (link)].
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6

Qualitative Evaluation of Accelerated ART Delivery

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Data were derived from in‐depth semi‐structured interviews (IDIs), using guides for health care provider interviews and key informant interviews that were designed to assess evidence for changes in providers’ attitudes and practices, and factors in the health systems context, that could facilitate uptake of accelerated ART delivery. For provider post‐intervention interviews, summary reports of pre‐intervention transcripts were prepared in order to tailor follow‐up questions. The guide included questions about providers’ perceptions of the intervention, and how it might have affected providers’ working conditions for the provision of ART. Interviews were conducted by two research assistants and a study coordinator who were trained by the lead investigator for the qualitative study. Key informant interviews were conducted by the lead investigator. Research team members were native speakers of local languages and conducted interviews in participants’ preferred language. The interviews were recorded and recordings were transcribed and translated into English, and loaded into ATLAS.ti software (version 7.1.7; GmbH, Berlin, Germany) for coding and analysis.
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7

Adolescent Mental Health Outcomes Analysis

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Quantitative data were analyzed using STATA software (version 14.2). Descriptive data involving continuous and categorical data were summarized using t-test and Chi-square test, respectively. Using ordinal logistic regression, we estimated the adjusted odds ratio (AOR) for experiencing mental health difficulties; including emotional, hyperactivity, conduct and peer relationship problems; as well as exhibiting prosocial behaviours. The ordinal levels were normal, borderline and abnormal. The independent variables were age, sex, residential status, psychosocial and financial support, academic performance, exposure to domestic violence, alcohol and illicit drugs. Two-tailed with p-value <0.05 and any confidence interval (CI) excluding 1.0 were statistically significant.
Qualitative interviews were audio-recorded, transcribed verbatim and the transcriptions exported into Atlas-ti software (version 7.1) for analysis. Codes were generated a priori based on the research questions plus evidence from literature, as well as inductively as new concepts emerged. The codes were carefully examined for recurring themes. After relevant themes were mapped out, related themes were grouped, leading to the completion of the thematic analysis. Important themes with accompanying quotes were extracted and reported.
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8

Qualitative Content Analysis of Interviews

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The interviews were then transcribed. Qualitative content analysis was used to gain a deeper understanding, compared to using descriptive analysis alone [31 (link)]. The analysis was performed in six steps: (1) The researcher listened to and read through the interviews several times to obtain an impression of all of the material; (2) Meaning units (words, sentences, or paragraphs related to each other through their content and context) were identified; (3) Meaning units were condensed to preserve relevant core expressions; (4) Units were coded and categories were divided into subcategories; (5) Categories were built from the subcategories; (6) After a process of interpretation, focusing on discovering the underlying meanings of the words or the content, categories were united in a comprehensive thematic framework [32 (link)]. ATLAS.ti software (ATLAS.ti Scientific Software Development GmbH, Berlin, Germany) was used to code and analyze the data. (Table 1)
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9

Constructive Grounded Theory Analysis of Caregiver Networks

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Process evaluation analysis of the informal caregiver networks will apply constructive grounded theory [56 ], run through the Atlas.ti® software Version 8.4.4. Within constructive grounded theory, data analysis and data collection take place simultaneously, which is why data analysis will begin with the first interviews. Sample size depends on theoretical saturation. This leading criterion for the grounded theory method can vary [56 ]. In this case, we assume that we will interview up to 30 relatives. Using comparative strategies (so-called “constant comparisons”), text passages, events, strategies and persons will be compared to identify content similarities and differences. Further analysis will be conducted by employing two analytical steps: initial and focused coding. A data segment will be coded row by row during initial coding to develop categories. In the next step (focused coding), the properties and connections of the codes will be developed so that an explanatory model of daily routines after the PAC programme is created in an inductive process. The process will be documented using memos to ensure the credibility and quality of the analysis [56 ].
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10

Physicians' Decision-Making for Oxycodone Requests

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Immediately after viewing the vignettes, all physician subjects completed a semi-structured interview concerning how they would manage the case, including their diagnoses for the sciatica patient, what medications they would prescribe, what non-medical treatments they would suggest, what additional information they would obtain directly from the patient and what testing they would pursue. Responses were provided by the responding physicians and later coded quantitatively and analyzed statistically.
For the active oxycodone requests only (n = 96), subjects took part in a qualitative interview that included two questions about the vignette patient specifically: “Can you tell me about how you made your decision about medications for the patient in the video? What role did the patient’s request have on your decision?” Answers were provided in a “think-aloud” format, allowing for insights into the cognitive reasoning behind the decision-making processes used by the physicians. Probing encouraged respondents to elaborate in directions they viewed as pertinent. Qualitative interviews (n = 95/96; one respondent did not agree to be recorded) were recorded digitally and transcribed verbatim by a professional transcription company. ATLAS.ti software (ATLAS.ti Scientific Software Development GmbH, Berlin, Germany) facilitated data organization and coding.
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