Maximizing Theoretical Contributions of Participant Observation While Managing Challenges
Bibliographic record
Abstract
Participant observation (PO) is an important method of data collection used in a variety of research methodologies. PO can inform theory development by providing understanding of participants' behaviors and the contexts that influence their behaviors. Because theory development is important in grounded theory studies, we emphasize theoretical contributions of PO while interrogating the challenges of using PO, in particular, attending to informed consent. We use the exemplar of a mid-range theory about nursing practice with hospitalized older adults to highlight contributions of PO to category development. While acknowledging theoretical contributions, we explore challenges entailed in observations where consenting participants interact with vulnerable patients and a changing cast of health care professionals in dynamic contexts. Reflexivity about interactions with vulnerable individuals, as well as other actions to avoid compromising voluntary consent, enhances contributions of PO.
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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 | 0.419 | 0.598 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.010 | 0.031 |
| Scholarly communication | 0.011 | 0.023 |
| Open science | 0.008 | 0.024 |
| Research integrity | 0.009 | 0.008 |
| 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, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".