Thematic Analysis of Using Visual Methods to Understand Healthcare Teams
Bibliographic record
Abstract
In healthcare, teams are essential in solving today’s toughest challenges. However, narrow disciplinary perspectives have limited our current understanding of how teams function in various healthcare contexts. Researchers often rely on traditional survey methods as their primary tool, which can prevent them from gathering comprehensive data. To overcome this limitation, sharing stories and narratives visually allows participants to create representations of their perceptions of team experiences and complex encounters using a variety of approaches. Despite the potential of this method, there is little empirical guidance on how to use it in health sciences research. To address this gap and provide guidance on using visual methods, particularly for analysing team function, we contacted researchers worldwide specializing in qualitative research methods. These researchers had published experience using visual methods. Over a year, three virtual, asynchronous brainstorming sessions were conducted with 16 researchers. The brainstorming sessions consisted of respondents receiving a survey with open-ended prompts to gather insight into the best practices for using visual methods. Researchers agreed that visual methods can gather implicit data, break down researcher-participant power dynamics, and improve a study’s accessibility to under-represented groups. It was highlighted, when interpreting drawings, co-analysis with the creator should be facilitated to mitigate biases and support interpretation. This use of visual methods can aid the creation of tacit and more nuanced descriptions of complex phenomena and improve team function through a deeper understanding and respect of each members’ needs/experiences/perspectives. Future studies should pilot visual methods with different healthcare teams to further investigate team dynamics and how findings can be used to optimize team function.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.031 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".