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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.100 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.017 | 0.014 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".