Identification of important respiratory research themes relevant to primary care: qualitative analysis of round 1 of the 2020 International Primary Care Respiratory Group (IPCRG) Research Prioritisation Exercise
Bibliographic record
Abstract
An update of the International Primary Care Respiratory Group (IPCRG) Research Needs Statement is currently being undertaken using an e-Delphi method. The aim of this analysis is to identify the main respiratory research themes from the perspective of primary care practitioners worldwide. Participants were recruited via the IPCRG network of 34 member countries. An initial open questionnaire elicited participants’ views on the most important respiratory conditions seen in their daily practice and invited suggestions of 5-10 relevant research questions within these conditions in the following domains: diagnosis, management, monitoring, self-management and prognosis. Using thematic qualitative analysis we identified the main cross-cutting research themes. 112 participants (69% physicians, 10% nurses, 21% other, 64% had special interest in respiratory) from 27 countries responded with 608 suggested research questions. Asthma was reported as the most clinically important condition (25.7%) followed by COPD (24.5%) and URTI (5.8%). Five themes emerged from the thematic analysis: uncertainties about diagnosis/management of respiratory conditions; need for contextually relevant and accessible guidance; need for methods to improve patient empowerment and self-management; role of the wider healthcare team; need for simple point-of-care tests. The eDelphi method is successful in identifying relevant research questions and the main themes pertinent to primary care worldwide. These research questions now need to be prioritised for investigation by the international community.
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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 | 0.047 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".