Bibliographic record
Abstract
PURPOSE: To test hypothesized relationships in Braden's Self-Help Model in a sample of people with idiopathic chronic pain. Testing theoretical explanations and predictions with various clinical populations under different sociocultural, economic, climatic, and health system environments provides evidence about the applicability of the underlying theory among diverse populations. DESIGN: This study was part of a larger randomized, controlled trial of a community-based nursing psycho-educational intervention: the Chronic Pain Self-Management Program (CPSMP). Pretest and posttest data were collected in 1995-1996 in Newfoundland, Canada. METHODS: Participants completed a battery of valid and reliable self-report instruments to measure pain, disability, uncertainty, resourcefulness, self-efficacy, adult role behaviors, and life satisfaction. Two tests of the self-help model were performed, the first with pretest data (N = 110) and the second with 3-month posttest data (N = 102) after half the sample had participated in the CPSMP intervention. Causal modeling path analysis was done with the Statistical Package for the Social Sciences (SPSS) program and tests of goodness-of-fit were conducted using EQS for Windows. RESULTS: Results supported the overall hypothesized pattern of relationships in the self-help model. Amounts of variance explained in the two major outcomes of the model (self-help: 42% to 53% and life quality: 45% to 47%) were consistent with results from other studies. Indices of goodness of fit showed an adequate fit of the model to the data, particularly the posttest data. CONCLUSIONS: Braden's Self-Help Model appears to be robust in a variety of clinical populations, among different settings and environmental conditions, and provides a useful framework to guide nursing psycho-educational interventions in chronic illness.
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 | 0.009 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".