Attachment, hope, and participation: Testing an expanded model of Snyder’s hope theory for prediction of participation for individuals with spinal cord injury.
Bibliographic record
Abstract
PURPOSE: The aim of the study was to test an expanded model of Snyder's hope theory for prediction of participation for individuals with spinal cord injury (SCI). Statistical model testing focused on evaluation of hope theory constructs (i.e., agency thoughts and pathways thoughts) as serial mediators of relationships between attachment and community participation. DESIGN: Quantitative, cross-sectional, descriptive design using multiple regression and correlational techniques. PARTICIPANTS: The sample comprised 108 persons with SCI recruited from spinal cord injury advocacy organizations in the United States, the United Kingdom, and Canada. RESULTS: Secure attachment, avoidant attachment, anxious attachment, and the hope constructs were significantly related to participation. Significant mediational effects were observed when agency thoughts and pathways thoughts were specified as mediators in series between attachment and community participation for people with SCI (i.e., agency specified as M1 and pathways specified as M2). CONCLUSION: Results provide support for Snyder's theoretical conceptualization and the use of hope-based interventions by rehabilitation practitioners for improving global participation outcomes for people with SCI who experience attachment-related difficulties. (PsycINFO Database Record
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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 | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".