Practical Issues in Using a Card Sort in a Study of Nonsupport and Family Caregiving
Bibliographic record
Abstract
The authors successfully used the card sort data collection technique with 17 female family caregivers in a large ethnographic study of non-support. In this article, they describe the practical issues they addressed. Initially, they developed strategies to construct meaningful statements that reflected key themes and were manageable in an interview. Later, to address challenges for participants, they developed approaches to assist women in moving beyond their own experience, dealing with test anxiety, and anticipating an emotional response. To facilitate effective data collection, they made detailed arrangements in advance, collected "talk aloud" data that captured women's decisions, and maintained accurate records. The women felt validated in their caregiving roles, as the card statements reflected their experience and rich data was elicited. The card sort exercise contributed information about variability in the meaning of similar interactions for different women and a beginning understanding of the criteria women used to make decisions.
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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 | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".