Experiences of older adults accessing specialized healthcare services in rural or remote areas: a systematic review protocol
Bibliographic record
Abstract
OBJECTIVE: The objective of this review is to synthesize the literature on the experiences of older adults accessing specialized healthcare services while living in remote or rural areas. INTRODUCTION: Older persons with chronic illnesses often need specialized healthcare services. Those who live in remote or rural areas may have limited access to these specialized healthcare services, potentially leading to an increase in morbidity and mortality. Little is known about the experience of older adults accessing specialized healthcare services while living in remote or rural areas. INCLUSION CRITERIA: This review will consider studies of persons 65 years and over who have self-identified as living in remote or rural areas. They will have on at least one occasion sought access in person to specialized healthcare services for a chronic condition(s) such as cardiovascular diseases, renal diseases, diabetes, cancers, mental illness or a major health concern beyond the scope of a primary care clinician, such as palliative care. METHODS: The search strategy will aim to find published and unpublished studies in English from 1980. Databases to be searched include: CINAHL, PubMed, PsycINFO, AgeLine, ProQuest Dissertations and Theses, Google Scholar and MedNar. Titles and abstracts will be screened against the inclusion criteria. Papers meeting the inclusion criteria will be appraised by two independent reviewers for methodological quality. Data will be extracted by two independent reviewers using a standardized data extraction tool. Findings will be pooled with the meta-aggregation approach, and a ConQual Summary of Findings will be presented.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
| gpt | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
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 | 0.097 | 0.075 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.017 | 0.014 |
| Bibliometrics | 0.018 | 0.013 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.048 | 0.007 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".