Protocol for a meta-integration: investigating positive aspects of caregiving in dementia
Bibliographic record
Abstract
INTRODUCTION: The current work describes the protocol for a meta-integration investigating the positive aspects of providing care to someone living with dementia. We aim to understand the position of positive aspects in the caregiver experience as well as identify how positive aspects are commonly conceptualised, investigated and measured in literature. METHODS AND ANALYSES: Meta-integration is a method of investigation that synthesises findings from meta-analysis or systematic review of quantitative studies and meta-synthesis of qualitative studies, to provide a coherent and holistic account of a phenomenon. As a relatively new method, terminology and methodological approaches are varied. The current work describes the process of conducting an advanced convergent meta-integration, including protocol for systematic search, inclusion/exclusion screening phases, intramethod analysis synthesis and intermethod synthesis on quantitative, qualitative and mixed methods research pertaining to the positive aspects of providing care to someone living with dementia. ETHICS AND DISSEMINATION: There are no ethics or safety concerns about dissemination, which includes plans for a conference presentation and publication.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.186 | 0.325 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.013 | 0.017 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.111 | 0.019 |
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".