Bringing it back to show and tell: combining visual and textual data to explore a psychological construct
Bibliographic record
Abstract
In this paper, we use two studies to reflect on how we explored the topic of athlete well-being and the challenges presented when attempting to identify the shared meanings and theoretical underpinnings of this context-specific psychological concept. We discuss how semi-structured interviews left us feeling stifled during data analysis. This spurred us to explore visual methods, specifically photo-elicitation, in our second study to help us address a language gap and further understand the shared meanings and theoretical underpinnings of a psychological concept. Interviews in conjunction with photographs helped us collect more nuanced data, enabling a more interpretive analysis of athlete well-being. Visuals can be a means to bridge a language gap when it can be difficult to articulate one’s experience. Lastly, we present participants’ reflections on their experiences of selecting photographs and how they perceived this to aid in their understanding and articulation of athlete well-being. We argue that collecting data about a psychological construct is more ambiguous and abstruse for participants compared to asking them about a personal event, experience, or moment. We suggest that time to reflect on the psychological construct, through the selection process of photo-elicitation, is vital in collecting data with more depth and detail which leads to the ability to complete a more interpretive analysis. Finally, we bring this back to discuss the full potential of qualitative methods; operating from aligned epistemological and ontological underpinnings to subjectively explore a psychological construct where participants can ascribe their own cultural and contextual meanings.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
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 | 0.025 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.014 | 0.021 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".