Consensus on interprofessional collaboration in hospitals: statistical agreement of ratings from ethnographic fieldwork and measurement scales
Bibliographic record
Abstract
RATIONALE: Few methods are available for analysing psychometric properties of combined qualitative and quantitative data. While conventional reliability of measures - meaning reproducibility or consistency - may not be meaningful in small-N research, in some health services studies agreement on perceptions arising from data generated by fieldwork and quantitative measures can be examined to good effect. METHODS: We studied interprofessional collaboration (IPC) in seven hospitals. An ethnographer shadowed and conducted interviews with regulated health professionals in medicine wards. Concurrently, nurses completed the nurse-doctor relations subscale of the Nursing Work Index (NWI-NDRS) and a new measurement scale for IPC with doctors in the domains of communication, accommodation, and isolation. After fieldwork, the ethnographer rank-ordered hospital sites on IPC from 1 to 7 based on interpretation of the qualitative data. Mean-scale scores were calculated for hospital sites and converted to ranks similarly. The Tinsley-Weiss T-index (Tinsley & Weiss, 1975) for agreement among rank orderings was calculated for dyadic combinations of fieldwork and measurement ranks. RESULTS: Perfect agreement was obtained for the most liberal agreement definitions considered - differences of two rank positions - involving qualitative data agreement with IPC subscales for accommodation and isolation. Defining agreement as a difference of 1 rank at most, the T-index was 0.77 for agreement between fieldworker and IPC accommodation and the same for NWI-NDRS and IPC isolation. CONCLUSION: Qualitative data from fieldwork rankings were substantially in accord with the contemporary IPC scales, less so with the NWI-NDRS. Qualitative data appear to be useful as an additional approach to confirming the validity of quantitative scale data in measuring a complex interpersonal relational construct.
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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 | 0.248 | 0.464 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".