Applying case study methodology to occupational science research
Bibliographic record
Abstract
Case study methodology offers a creative and flexible way to gain a comprehensive understanding of human complexities in context, using various means to collect data. This paper is divided into two parts. Part one provides a brief overview of what case study methodology is; and part two presents an integrated review (Whittemore & Knafl, 2005) on how case study has been used for the study of occupation. Findings indicate that while case study methodology is increasingly used for the study of occupation, many of its essential features are absent in published research, such as a definition of the bounded case in its context, use of multiple sources of data, and detailed information about the research process in the output. Recommendations are provided on these essential features of case study to advance the effective use of case study methodology for studying occupation.
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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 | 0.075 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".