Protocol for development, calibration and validation of the Patient-Reported Inventory of Self-Management of Chronic Conditions (PRISM-CC)
Bibliographic record
Abstract
INTRODUCTION: Assessing and measuring patients' chronic condition self-management needs are critical to quality health care and to related research. One in three adults around the world live with multiple chronic conditions. While many patient-reported measures of self-management have been developed, none has emerged as the gold standard, and all have one or more of the following limitations: (1) they fail to measure the different domains of self-management important to patients, (2) they lack sufficient specificity to support patient-centred care or identify the specific components of self-management interventions that work and/or (3) they lack suitability for patients with multiple chronic conditions. METHODS AND ANALYSIS: onditions (PRISM-CC) is being developed to overcome these shortcomings. It will measure respondents' perceived success (or difficulty) in self-managing seven domains important to patients. The protocol has three phases. Phase 1 is conceptual model development and item generation. Phase 2 is assessment of the relevance and understanding of items by people with chronic conditions. Phase 3 is item analysis, dimensionality assessment, scaling and preliminary validation of the PRISM-CC using an online survey of people with chronic conditions (n~750). The expected completion date is early 2021. ETHICS AND DISSEMINATION: This study will adhere to the Canadian Tri-Council Policy Statement on Ethical Conduct for Research Involving Humans. Ethics approval for all phases has been obtained from the Nova Scotia Health Authority Research Ethics Board. Once completed, the PRISM-CC will be made available for research and healthcare at minimal to no cost.
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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 | 0.118 | 0.139 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.091 | 0.029 |
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".