Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVES: There are a growing number of pain self-management applications (apps) available for users to download on personal smartphones. The purpose of this study was to critically appraise the content and self-management functionality of currently available pain apps. METHODS: An electronic search was conducted between May and June 2014 of the official stores for the 4 major operating systems. Two authors independently identified patient-focused apps with a stated goal of pain management. Discrepancies regarding selection were resolved through discussion with a third party. Metadata from all included apps were abstracted into a standard form. The content and functionality of each app as it pertained to pain self-management was rated. RESULTS: A total of 279 apps met the inclusion criteria. Pain self-care skill support was the most common self-management function (77.4%). Apps also purported providing patients with the ability to engage in pain education (45.9%), self-monitoring (19%), social support (3.6%), and goal-setting (0.72%). No apps were comprehensive in terms of pain self-management, with the majority of apps including only a single self-management function (58.5%). In addition, only 8.2% of apps included a health care professional in their development, not a single app provided a theoretical rationale, and only 1 app underwent scientific evaluation. DISCUSSION: Currently available pain self-management apps for patients are simplistic, lack the involvement of health care professionals in their development, and have not been rigorously tested for effectiveness on pain-related health outcomes. There is a need to develop and test theoretically and evidence-based apps to better support patients with accessible pain care self-management.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.073 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it