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Record W2948835971 · doi:10.1186/s13023-019-1103-8

“Be an ambassador for change that you would like to see”: a call to action to all stakeholders for co-creation in healthcare and medical research to improve quality of life of people with a neuromuscular disease

2019· article· en· W2948835971 on OpenAlexaff
Anna Ambrosini, Rosaline C. M. Quinlivan, Valeria Sansone, Ingeborg Meijer, Guus Schrijvers, Aad Tibben, George W. Padberg, Maarten de Wit, Ellen Sterrenburg, Alexandre Méjat, Alexandra Breukel, Michal Rataj, Hanns Lochmüller, Raffaella Willmann

Bibliographic record

VenueOrphanet Journal of Rare Diseases · 2019
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsOttawa HospitalChildren's Hospital of Eastern OntarioUniversity of Ottawa
FundersEuropean Neuromuscular CentreMuscular Dystrophy Association
KeywordsGeneral partnershipHealth carePublic relationsBiobankCall to actionMedicineMedical educationBusinessNursingPolitical scienceMarketing

Abstract

fetched live from OpenAlex

BACKGROUND: Patient and public involvement for co-creation is increasingly recognized as a valuable strategy to develop healthcare research targeting patients' real needs. However, its practical implementation is not as advanced and unanimously accepted as it could be, due to cultural differences and complexities of managing healthcare programs and clinical studies, especially in the rare disease field. MAIN BODY: The European Neuromuscular Centre, a European foundation of patient organizations, involved its key stakeholders in a special workshop to investigate the position of the neuromuscular patient community with respect to healthcare and medical research to identify and address gaps and bottlenecks. The workshop took place in Milan (Italy) on January 19-20, 2018, involving 45 participants who were mainly representatives of the patient community, but also included experts from clinical centers, industry and regulatory bodies. In order to provide practical examples and constructive suggestions, specific topics were identified upfront. The first set of issues concerned the quality of life at specific phases of a patient's life, such as at the time of diagnosis or during pediatric to adult transition, and patient involvement in medical research on activities in daily living including patient reported outcome measures. The second set of issues concerned the involvement of patients in the management of clinical research tools, such as registries and biobanks, and their participation in study design or marketing authorization processes. Introductory presentations were followed by parallel working group sessions, to gain constructive contributions from all participants. The concept of shared decision making was used to ensure, in discussions, a partnership-based identification of the wishes and needs of all stakeholders involved, and the "ladder of participation" tool served as a model to evaluate the actual and the desired level of patients' involvement in all topics addressed. A general consensus on the outcome of the meeting was collected during the final plenary session. This paper reports the outcome of the workshop and the specific suggestions derived from the analysis of the first set of topics, related to quality of life. The outcomes of the second set of topics are reported elsewhere and are only briefly summarized herein for the sake of completeness. CONCLUSIONS: The neuromuscular community proved to be very active and engaged at different levels in the healthcare initiatives of interest. The workshop participants critically discussed several topics, providing practical examples where different stakeholders could play a role in making a change and bridging gaps. Overall, they indicated the need for education of all stakeholders for better communication, where everyone should become an ambassador to promote real change. Support should also come from institutions and healthcare bodies both at structural and economic level.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.038
Threshold uncertainty score0.576

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.439
GPT teacher head0.523
Teacher spread0.084 · 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 teacher head, 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

Citations20
Published2019
Admission routes1
Has abstractyes

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