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Record W2062212156 · doi:10.1002/mdc3.12037

Challenges Faced by Patients With Progressive Supranuclear Palsy and their Families

2014· article· en· W2062212156 on OpenAlexaff
Theresa HM Moore, Mark Guttman

Bibliographic record

VenueMovement Disorders Clinical Practice · 2014
Typearticle
Languageen
FieldHealth Professions
TopicDysphagia Assessment and Management
Canadian institutionsCentre for Movement Disorders
Fundersnot available
KeywordsFocus groupQualitative researchNursingMedicineService providerService (business)PsychologyFamily medicine

Abstract

fetched live from OpenAlex

The literature is inadequate for understanding the challenges experienced by people with PSP and their families. Therefore, the aim of this study was to understand the challenges of people with PSP and their caregivers and identify their priority need. In this qualitative study, five focus groups were conducted with people with PSP and/or their family caregivers, one group with long-term care staff, and one with community caregivers. Data were analyzed using fundamental qualitative description. Four themes were identified: knowledge, services, research, and symptoms. Knowledge challenges were identified as the priority need, with the most common challenges in this category being lack of knowledge of PSP among community workers, physicians, patients, and family members. Service challenges involved service access and interactions with physicians, community workers, private caregivers, and long-term care staff. Research challenges related to the lack of research and the failure of health care providers or PSP organizations to communicate research findings. Symptoms most often identified as challenging were falls, mobility, vision, mood or thinking, speech, and swallowing. Participants identified their priority need as dissemination of information about PSP. This has not been captured in previous research. This information needs to reach doctors, long-term care staff, community workers, patients, families, and the general public. Subsequent activities to meet this need are summarized. These activities resulted in three new resources: a brochure for patients and families; an information packet for physicians; and a webinar for staff in long-term care and community.

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)0.009
metaresearch head score (Gemma)0.028
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.028
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0100.005
Scholarly communication0.0040.004
Open science0.0010.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.041
GPT teacher head0.413
Teacher spread0.373 · 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
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

Citations11
Published2014
Admission routes1
Has abstractyes

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