Interrelationship of brain fog, pain, and psychological distress with quality of life of Veterans with painful symptoms
Bibliographic record
Abstract
Introduction: Brain fog is a phenomenon experienced by persons with pain, described as a mental cloudiness associated with cognitive challenges (e.g., remembering information) that may result in reduced participation. Canadian Veterans are twice as likely as civilians to experience chronic pain and, consequently, brain fog. The experience of brain fog is also described by people with other conditions (e.g., traumatic stress) that are common among Veterans with chronic pain. The impact of brain fog from chronic pain among Veterans has not been qualitatively described. The purpose of this study was to describe the phenomenon and impacts of brain fog among Veterans with chronic pain. Methods: The study used a qualitative descriptive approach, guided by a constructivist theoretical lens. Focus groups were conducted to generate data, and verbatim transcripts were analyzed using an inductive content and gender matrix analysis. Results: Twenty-five Veterans (six women, 19 men) across Canada participated in this study. Three descriptive categories were identified: 1) brain fog experiences, 2) reciprocal and linear relationships of the triggers, impacts, and strategies to manage brain fog, and 3) barriers to and solutions for management. The matrix analysis identified gender differences in impacts and management strategies. Discussion: Brain fog was described as a fluctuating experience that could generate reciprocal cognitive and emotional impacts, creating challenges with meaningful engagement and goal achievement. To reduce the burden of this experience and restore quality of life, Veterans emphasized the need for awareness, development of tools to measure brain fog, and modifications to existing interventions.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".