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Record W3014168532 · doi:10.1097/nur.0000000000000517

Exploring the Perceived Self-management Needs of Young Adults With Osteogenesis Imperfecta

2020· article· en· W3014168532 on OpenAlexaff
Alisha Michalovic, Charlotte Emily Ewels Anderson, Kelly Thorstad, Frank Rauch, Argerie Tsimicalis

Bibliographic record

VenueClinical Nurse Specialist · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicConnective tissue disorders research
Canadian institutionsShriners Hospitals for Children - Canada
Fundersnot available
KeywordsMentorshipYoung adultOsteogenesis imperfectaTransitional careMedicineHealth carePediatric Nurse PractitionerGeneral partnershipFamily medicineJungleQualitative researchNursingNurse practitionersGerontologyMedical education

Abstract

fetched live from OpenAlex

PURPOSE: To explore the perceived self-management needs of young adults with osteogenesis imperfecta (OI) with the goal of optimizing the self-management and transitional care services. METHODS: A qualitative descriptive study was performed with young adults diagnosed with OI. Two semistructured interviews were conducted before and after their first appointment with a nurse practitioner in the adult healthcare settings (a new partnership initiated by the pediatric hospital). Data were transcribed and descriptively analyzed. RESULTS: Seven participants with OI types I, III, and IV were interviewed. Ages ranged from 23 to 34 years, and years since discharge from the pediatric hospital ranged from 3 to 10. Four themes emerged including (1) dropped in the jungle, with no one to call; (2) they do not know how to treat me; (3) I feel like I'm going to get back in the loop; and (4) self-managing what I know, how I know. CONCLUSIONS: Similar to other childhood-onset conditions, adolescents and young adults with OI require education and mentorship, and clinicians in the adult healthcare system need to be prepared and supported to receive them. Collective efforts are needed to improve the self-management and transitional care needs for young adults with OI.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.481
Threshold uncertainty score0.478

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.056
GPT teacher head0.324
Teacher spread0.269 · 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

Citations15
Published2020
Admission routes1
Has abstractyes

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