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Record W2977706402 · doi:10.2196/14039

Spinal Cord Injury Veterans’ Disability Benefits, Outcomes, and Health Care Utilization Patterns: Protocol for a Qualitative Study

2019· article· en· W2977706402 on OpenAlexvenueno aff
Denise Fyffe, Joyce Williams, Paul Tobin, Carol Gibson‐Gill

Bibliographic record

VenueJMIR Research Protocols · 2019
Typearticle
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsnot available
FundersMedical Research and Materiel CommandCongressionally Directed Medical Research ProgramsSRA InternationalU.S. Department of Defense
KeywordsSpinal cord injuryProtocol (science)MedicineHealth careQualitative researchPhysical therapyPsychologyNursingGerontologyPhysical medicine and rehabilitationAlternative medicinePsychiatrySpinal cord

Abstract

fetched live from OpenAlex

BACKGROUND: An estimated 42,000 people currently living with chronic spinal cord injury (SCI) are veterans. SCI was a common combat-related injury in the World Wars and Vietnam era and now affects more than 11% of military personnel injured in Operation Iraqi Freedom and Operation Enduring Freedom. The Veterans Benefits Administration primarily offers financial compensation for disabilities sustained or re-aggravated during military service, called service-connected disability compensation. With the overwhelming cost of living with an SCI, this monthly financial compensation can provide service-connected veterans and their families with access to additional supportive resources (eg, assistive devices and personal aide) and maintain their quality of life (QOL). Little is known about personal, health, functional, and QOL outcomes associated with service-connected and nonservice-connected status for veterans living with an SCI. OBJECTIVE: The aim of this study is to compare the ways in which Veterans Affairs' (VA) service-connected and nonservice-connected status may be associated with health and functional outcomes, choice of health care provider, and overall QOL for veterans living with an SCI and their caregivers. METHODS: This cross-sectional qualitative study will gather data using retrospective chart reviews, semistructured interviews, and focus groups. After obtaining institutional review board (IRB) approval, purposeful sampling techniques will be used to recruit and enroll the following key stakeholders: veterans living with an SCI, family caregivers, and SCI health care providers. Concurrent data collection will take place at 2 sites: Veterans Administration New Jersey Healthcare System and Northern New Jersey Spinal Cord Injury System. RESULTS: This study was funded in July 2015. IRB approval was obtained by November 2016 at both sites. Enrollment and data collection for phase 1 to phase 4 are complete. A total of 69 veterans, 18 caregivers, and 19 SCI clinicians enrolled in the study. Data analyses for these phases are underway. In phase 5, the follow-up focus group activities are scheduled. The final results are expected by the end of 2019. CONCLUSIONS: The factors that contribute to veterans living with SCI seeking and not seeking VA disability compensation benefits are not well understood in rehabilitation research. Triangulation of these data sources will allow us to compare, contrast, and integrate the results, which can be used to develop clinical guidelines, caregiver training, and patient education programs. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/14039.

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 imitation

Not 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.

metaresearch head score (Codex)0.056
metaresearch head score (Gemma)0.034
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.056
Threshold uncertainty score0.298

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0560.034
Meta-epidemiology (narrow)0.0030.003
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0040.004
Science and technology studies0.0090.004
Scholarly communication0.0040.004
Open science0.0050.005
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0530.006

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.

Opus teacher head0.622
GPT teacher head0.709
Teacher spread0.088 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreProtocol

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".

Quick stats

Citations4
Published2019
Admission routes1
Has abstractyes

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