Conceptualisation and measurement of adaptation within the Roy adaptation model in chronic care: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: The Roy adaptation model provides a basis for developing the science of nursing. Its theoretical assumptions have been tested in empirical studies. Although several works have historically reviewed the development of this model, a refinement of its key concepts is needed. The proposed scoping review aims to describe how the concept of adaptation was defined and measured in nursing studies related to chronic health conditions. METHODS AND ANALYSIS: This scoping review will adopt the methodology proposed by Arksey and O'Malley. Several databases, including MEDLINE (OVID), CINAHL, EMBASE, PsycINFO, PubMed, Wan Fang, China National Knowledge Infrastructure and VIP net, will be selected and used to mine literature published in English and Chinese languages, up to December 2019. Key terms related to 'Roy adaptation model' will be identified and used for developing tailored search strategies for each database. Articles will be included in the analysis if they are primary research reports explaining the concept of adaptation within the field of chronic care. All screening and extraction of literature will be independently performed and checked by two authors, according to the guideline of Preferred Reporting Items for Systematic Review and Meta-Analysis-Extension for Scoping Reviews. The findings will be organised and summarised into narratives in line with the construction of conceptual-theoretical-empirical system of knowledge for further consultation and translation. ETHICS AND DISSEMINATION: This scoping review does not require ethical approval. The findings are expected to be published in peer-reviewed English or Chinese journals as well as conference proceedings in the area of chronic care.
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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 | 0.148 | 0.114 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.021 | 0.016 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.057 | 0.015 |
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".