Development of a Research Tool to Document Self-Reported Chronic Conditions in Primary Care
Bibliographic record
Abstract
BACKGROUND: Researchers interested in multimorbidity often find themselves in the dilemma of identifying or creating an operational definition in order to generate data. Our team was invited to propose a tool for documenting the presence of chronic conditions in participants recruited for different research studies. OBJECTIVE: To describe the development of such a tool. DESIGN: A scoping review in which we identified relevant studies, selected studies, charted the data, and collated and summarized the results. The criteria considered for selecting chronic conditions were: (1) their relevance to primary care services; (2) the impact on affected patients; (3) their prevalence among the primary care users; and (4) how often the conditions were present among the lists retrieved from the scoping review. RESULTS: Taking into account the predefined criteria, we developed a list of 20 chronic conditions/categories of conditions that could be self-reported. A questionnaire was built using simple instructions and a table including the list of chronic conditions/categories of conditions. CONCLUSIONS: We developed a questionnaire to document 20 self-reported chronic conditions/categories of conditions intended to be used for research purposes in primary care. Guided by previous literature, the purpose of this questionnaire is to evaluate the self-reported burden of multimorbidity by participants and to encourage comparability among research studies using the same measurement.
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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 | 0.140 | 0.361 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.042 | 0.017 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".