Identifying gaps in knowledge: A map of the qualitative literature concerning life with a neurological condition
Bibliographic record
Abstract
OBJECTIVES: To describe patterns in the qualitative literature regarding the everyday experience of living with a neurological condition; to identify areas of depth as well as gaps in the existing knowledge base. METHODS: An extensive search of the literature yielded 474 articles meeting the inclusion criteria. Data extraction, based on scrutiny of both abstract and full text article included country of origin, diagnosis, stated aim, methodological framework/design, participants, and data collection method(s). Studies were categorized into 27 topics within four broad foci. RESULTS: Four broad foci describe the field: impact and management, daily activities and occupations, impact on family, and the healthcare experience. Overall the research is unevenly distributed by diagnosis; some are well represented while others are the subject of little research. Even diagnoses well represented in quantity can be limited in breadth. DISCUSSION: Possible explanations for the patterns of emphasis include: a focus on issues and problems, highlighted points of contact between patients and healthcare providers, and ability of participants to voice their views. The literature is also characterized by limited across diagnoses research or that comparing the experience of people with different diagnoses. There is a need for more research in particular diagnoses; more varied data collection methods and acknowledgement of ethnicity, gender, discrimination, and social inequalities.
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 | 0.037 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.034 | 0.034 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".