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Livia Weigel<p>säggsisch Transkript nacharbeiten macht Garnelen spaß</p><p>...eben weil du regelmäßig so Dinge wie "Garnelen" zu "gar keinen" Spaß korrigieren musst <a href="https://sciences.social/tags/whisper" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>whisper</span></a> <a href="https://sciences.social/tags/soziologie" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>soziologie</span></a> <a href="https://sciences.social/tags/qualitativeresearch" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>qualitativeresearch</span></a></p>
Thomas Herrmann<p>Bei der heutigen Tooltime ging es um noScribe. Automatisierte Interviewtranskription, datenschutzfreundlich &amp; <a href="https://higher-edu.social/tags/opensource" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>opensource</span></a> , mit sehr viel Potential in Forschung und Lehre. Dementsprechend große Resonanz unter den Kolleg*innen an der Fakultät.<br>Jetzt bich mal gespannt, von welchen Erfahrungen sie mir in 1/2 Jahr berichten 🙂.<br><a href="https://higher-edu.social/tags/HigherEducation" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>HigherEducation</span></a> <a href="https://higher-edu.social/tags/qualitativeresearch" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>qualitativeresearch</span></a> <a href="https://higher-edu.social/tags/whisperai" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>whisperai</span></a></p>
Jan R. Boehnke<p>About once a year I need to figure out which <a href="https://mastodon.social/tags/NVivo" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>NVivo</span></a> licence we are subscribed to.</p><p>As the company does not have a clear version history table on their homepage, this necessitates a visit to their user forum to find the entries w the info I need:<br>"Mate, v14 comes after v20!" 😉</p><p><a href="https://mastodon.social/tags/QualitativeResearch" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>QualitativeResearch</span></a> <a href="https://mastodon.social/tags/SoftwareVersioning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>SoftwareVersioning</span></a></p>

I am proud to announce that the pre-release of OpenQDA is now live. #OpenQDA is an #opensource qualitative coding software that we are developing at @zemki . It can now import and export codebooks from other #QDA programs and autotranscribe. #qualitativeresearch #qualitativedata #sociology #communications #groundedtheory #qualitativeresearch #qualitativedata @sociology @communicationscholars @unibremen For more information: zemki.uni-bremen.de/en/researc

Spread the word: #OpenQDA is available as early access. It’s a new software for #coding of #qualitative #data such as #interviews, #protocols and others. OpenQDA is clear and easy to use, you can work together as a team in real time, extend everything via plugins - and it's #opensource and #freesoftware. Join our #community! #qda #qualitativeresearch #qualitativedata #sociology #communications #groundedtheory @zemki @sociology @communicationscholars @unibremen openqda.org/

Led by Phuong Bich Tran, this COSMOS* network study supports the growing appreciation that people's views about what is important in #HealthCare differ from those of clinicians/researchers
gh.bmj.com/content/9/1/e013606

Re-analysing 50 semi-structured interviews from 10 LMICs across 3 regions showed especially how important finances, financing, and loss of income are.

#GlobalHealth #Multimorbidity #UniversalHealthCoverage #QualitativeResearch

*bmjopen.bmj.com/content/12/2/e

BMJ Global Health · An interpretative phenomenological analysis of the lived experience of people with multimorbidity in low- and middle-income countriesPeople living with multimorbidity (PLWMM) have multiple needs and require long-term personalised care, which necessitates an integrated people-centred approach to healthcare. However, people-centred care may risk being a buzzword in global health and cannot be achieved unless we consider and prioritise the lived experience of the people themselves. This study captures the lived experiences of PLWMM in low- and middle-income countries (LMICs) by exploring their perspectives, experiences, and aspirations. We analysed 50 semi-structured interview responses from 10 LMICs across three regions—South Asia, Latin America, and Western Africa—using an interpretative phenomenological analysis approach. The bodily, social, and system experiences of illness by respondents were multidirectional and interactive, and largely captured the complexity of living with multimorbidity. Despite expensive treatments, many experienced little improvements in their conditions and felt that healthcare was not tailored to their needs. Disease management involved multiple and fragmented healthcare providers with lack of guidance, resulting in repetitive procedures, loss of time, confusion, and frustration. Financial burden was exacerbated by lost productivity and extreme finance coping strategies, creating a vicious cycle. Against the backdrop of uncertainty and disruption due to illness, many demonstrated an ability to cope with their conditions and navigate the healthcare system. Respondents’ priorities were reflective of their desire to return to a pre-illness way of life—resuming work, caring for family, and maintaining a sense of independence and normalcy despite illness. Respondents had a wide range of needs that required financial, health education, integrated care, and mental health support. In discussion with respondents on outcomes, it appeared that many have complementary views about what is important and relevant, which may differ from the outcomes established by clinicians and researchers. This knowledge needs to complement and be incorporated into existing research and treatment models to ensure healthcare remains focused on the human and our evolving needs. Data are available upon reasonable request and subject to approval by the COSMOS group. Part of the data is available in online supplemental file 4.

I am SO excited about this upcoming @CenterforOpenScience webinar on sharing sensitive qualitative data with the talented Rebecca Campbell, which will be on February 15 at 11am US Eastern!

Register:
cos-io.zoom.us/webinar/registe

Read her team's paper here: journals.sagepub.com/doi/full/

ZoomWelcome! You are invited to join a webinar: Sharing Sensitive Qualitative Data. After registering, you will receive a confirmation email about joining the webinar.Researchers who handle qualitative and/or sensitive research data have many questions on whether to share data, and how to share data in a way that aligns with their ethical obligations. While working with a dataset involving interviews with sexual assault survivors, a team led by Dr. Rebecca Campbell developed a methodological framework for remediating sensitive narrative data. During this multiphase process, the team talked to diverse community partners to understand reidentifiability risks and data sharing concerns. They then engaged in an iterative process to identify potentially identifiable information and problem-solved through group review and consensus. Finally, they assessed the validity of the deidentified analyses. Join us as their team describes their process, considerations for researchers wanting to share sensitive narrative data, and ways that funders and institutions can support researchers doing this work.

I've been waiting for a paper like this to come out: arxiv.org/abs/2309.06364
Can LLMs (here ChatGPT 3.5) simulate qualitative interview respondents?
The authors seem to think this may actually relevant to conduct actual qual research using LLM responses which seems... uninteresting to me?
OTOH I think at least in three areas LLM answers could be relevant if they're good:
1. Training coders
2. Development of initial coding schemes
3. (Alas) detecting fraud
#qualitativeResearch

1/2

arXiv.orgFramework-Based Qualitative Analysis of Free Responses of Large Language Models: Algorithmic FidelityToday, using Large-scale generative Language Models (LLMs) it is possible to simulate free responses to interview questions like those traditionally analyzed using qualitative research methods. Qualitative methodology encompasses a broad family of techniques involving manual analysis of open-ended interviews or conversations conducted freely in natural language. Here we consider whether artificial "silicon participants" generated by LLMs may be productively studied using qualitative methods aiming to produce insights that could generalize to real human populations. The key concept in our analysis is algorithmic fidelity, a term introduced by Argyle et al. (2023) capturing the degree to which LLM-generated outputs mirror human sub-populations' beliefs and attitudes. By definition, high algorithmic fidelity suggests latent beliefs elicited from LLMs may generalize to real humans, whereas low algorithmic fidelity renders such research invalid. Here we used an LLM to generate interviews with silicon participants matching specific demographic characteristics one-for-one with a set of human participants. Using framework-based qualitative analysis, we showed the key themes obtained from both human and silicon participants were strikingly similar. However, when we analyzed the structure and tone of the interviews we found even more striking differences. We also found evidence of the hyper-accuracy distortion described by Aher et al. (2023). We conclude that the LLM we tested (GPT-3.5) does not have sufficient algorithmic fidelity to expect research on it to generalize to human populations. However, the rapid pace of LLM research makes it plausible this could change in the future. Thus we stress the need to establish epistemic norms now around how to assess validity of LLM-based qualitative research, especially concerning the need to ensure representation of heterogeneous lived experiences.