Qualitatively Pre-Testing a Tailored Financial Literacy Measurement Instrument for Professional Athletes
Bibliographic record
Abstract
The aim of this study was to qualitatively pre-test a research instrument to assess the financial literacy skills of professional athletes who compete in a team sport environment. Questions were developed based on a review of the current literature and an analysis of qualitative data from twelve structured expert interviews, selected using actor–network theory and purposive sampling. The findings showed how qualitative data can be considered and enumerated to guide the development of 28 validated questions to assess financial literacy within a specific group. This study helps to fill a gap in the literature since there is a paucity of qualitatively mediated research that focuses on specific target groups in the field of financial literacy. This research instrument could be of value to professional athletes, sports club management, players’ associations, educators, researchers, sports agents, and advisors by providing them with a greater understanding of their clients’ financial literacy skills and financial needs.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".