Using participant observation in pediatric health care settings: ethical challenges and solutions
Bibliographic record
Abstract
Participant observation strategies may be particularly effective for research involving children and their families in health care settings. These techniques, commonly used in ethnography and grounded theory, can elicit data and foster insights more readily than other research approaches, such as structured interviews or quantitative methods. This article outlines recommendations for the ethical conduct of participant observation in pediatric health care settings. This involves a brief overview of the significant contributions that participant observation can bring to our understanding of children and families in health care settings; an examination of the elements of participant observation that are necessary conditions for its effective conduct; an outline of contemporary ethical norms in Canada, the United Kingdom and the United States for research in pediatric health care settings; and a discussion of how participant observation research should be operationalized in order to comply with these norms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.358 | 0.321 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.007 | 0.024 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".