Assessment of Rigour in Published Nursing Intervention Studies that Use Observational Methods
Bibliographic record
Abstract
Unstructured observation involving “going into the field” to describe and analyze what is seen and heard, may be an underutilized method in nursing research. The role of the observer, the nature of the observations, data sources, systematic recording and analysis of observations, appropriate analysis of the data, and corroboration of findings are important considerations when ensuring rigour in observational methods. However, the description of observational techniques and methods provided in published accounts of qualitative research is sparse, and it is therefore difficult to evaluate the truthfulness, credibility, and trustworthiness of many research studies. Observational methods can address discrepancies between what people say and what they actually do, and they can capture the context in which nurses practice. Little is known about the oral hygiene care practices of nurses caring for hospitalized older adults with longer lengths of stay, despite the link between poor oral hygiene and systemic illness. To date, the oral hygiene care provided by nurses has not been directly observed, nor have unstructured observational techniques been used to observe any caregivers providing such interventions. In the absence of studies related to oral hygiene care, an integrative review of the literature has been undertaken to critically analyze how rigour was ensured in qualitative or mixed - methods studies in which observational methods were used to study nurses as they provided other types of basic nursing interventions. Whittemore and Knafl’s revised integrative review method was utilized, and criteria that would indicate rigour in a study were gleaned from the literature to create a framework for analysis.
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 | 0.776 | 0.866 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.011 | 0.012 |
| Bibliometrics | 0.023 | 0.017 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.008 | 0.011 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".