MétaCan
Menu
Back to cohort
Record W4405401308 · doi:10.2196/59032

Evaluation of Financial Support Workshops for Patients Under State Pension Age With Degenerative Cervical Myelopathy: Survey Study

2024· article· en· W4405401308 on OpenAlexvenueno aff
Tanzil Rujeedawa, Zahabiya Karimi, Helen Wood, Irina Sangeorzan, Roy C. Smith, Iwan Sadler, Esther Martin‐Moore, Adrian Gardner, Andreas K. Demetriades, Rohitashwa Sinha, Gordan Grahovac, Antony Bateman, Naomi D Deakin, Benjamin M. Davies

Bibliographic record

VenueJMIR Formative Research · 2024
Typearticle
Languageen
FieldMedicine
TopicCervical and Thoracic Myelopathy
Canadian institutionsnot available
Fundersnot available
KeywordsPreprintPensionMyelopathyMedicinePhysical therapyGerontologyFinanceBusinessPsychiatrySpinal cordComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

Background: Degenerative cervical myelopathy (DCM), a form of slow-motion and progressive spinal cord injury caused by spinal cord compression secondary to degenerative pathology, leads to high levels of disability and dependence, and may reduce quality of life. Myelopathy.org is the first global scientific and clinical charity for DCM, providing an accessible platform freely disseminating information relevant to the DCM diagnosis and its treatment. Significant transient and long-term change to earnings do occur and can thrust individuals into poverty. People with DCM face many challenges accessing state financial assistance. This can have a cumulative negative financial effect due to the association between DCM and low socioeconomic index. Financial support available to patients under pension age include Universal Credit (UC), a payment that helps with living costs, and Personal Independence Payment (PIP), which helps with extra living costs if someone has both a long-term health condition or disability and difficulty doing certain everyday tasks. Objective: This study aimed to assess if delivering workshops centered around access to financial support could assist people with DCM living in the United Kingdom. Methods: A series of 2 internet-based workshops was targeted at accessing financial support for English patients under the state pension age, with an anonymized survey delivered to participants after each session. The first session was on UC and the second on PIP. The survey consisted of a mixture of Likert scales, free text and yes or no answers. Survey responses were analyzed using descriptive statistics and free text answers underwent inductive thematic analysis. Results: The average rating on the use of UC was 9.00/10. Presession self-rated confidence levels were 5.11/10 rising to 8.00/10. The mean score of wanting further similar sessions was 8.67/10 with 56% (5/9) of participants wanting one-to-one sessions. For PIP, the average session use rating was 10/10. Presession self-rated confidence levels were 4.43/10 rising to 9.57/10. The mean score of wanting further similar sessions was 8.71/10, with 43% (3/7) of participants wanting one-to-one sessions . Following inductive thematic analysis, themes regarding the usefulness of such sessions and the challenges to accessing financial support emerged. One participant gave negative feedback, which included the length of the session and perceived problems around confidentiality and data protection. Conclusions: The pilot series was largely perceived as a success, with participants finding them useful and increasing their self-rated confidence in navigating the UK financial support system. Given the small sample size, it is hard to predict the success of future sessions. Finally, given that the hurdles in accessing financial support extend beyond DCM, such workshops may be relevant to other organizations.

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)0.010
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.134
GPT teacher head0.451
Teacher spread0.318 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueJMIR Formative ResearchSame topicCervical and Thoracic MyelopathyFrench-language works237,207