Creating and vetting survey items for coaches of masters athletes from qualitative exploratory studies
Bibliographic record
Abstract
Callary, Rathwell, and Young (2015, 2017) interviewed Masters Athletes (MAs) and coaches in an open-ended and non-theoretical manner, and conducted interpretative phenomenological and thematic analyses that developed themes in line with adult learning principles. The purpose of this presentation is to outline the development of a measurement tool asking coaches to self-report how frequently they use approaches that are commensurate with adult teaching principles that a small sample of Masters swimmers have indicated they want and need from their coaches (Callary et al., 2015), and that a small sample of coaches of Masters swimmers have perceived that they variably deliver (Callary et al., 2017). We also present findings of the vetting procedure in the validation of the instrument. The three researchers suggested and collaboratively vetted 67 items across 12 themes built from their qualitative findings, flagging and subsequently cutting 17 items. The items were emailed to 12 known expert coaches of MAs to ask them to consider, on a scale of one to four, how much they agree that each item, as written, makes sense and is not awkward, and pertains to coaching MAs. Two items were flagged based on coaches' responses, but after our discussions, remained in the pool to ensure a minimum of three items per theme. The findings suggest initial content validity for survey items that will provide a descriptive profile for the use of andragogic learning principles by sport coaches working with MAs.
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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 | 0.143 | 0.252 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.013 | 0.007 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".