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Enregistrement W1547706747 · doi:10.1111/jgs.13450

Aging in Orange

2015· article· en· W1547706747 sur OpenAlexaboutno aff
Marielle Bolano

Notice bibliographique

RevueJournal of the American Geriatrics Society · 2015
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueCriminal Justice and Corrections Analysis
Établissements canadiensnon disponible
Organismes subventionnairesNational Institute on AgingUniversity of California, San Francisco
Mots-clésMedicineGeriatricsGerontologyCognitive impairmentPopulationAnxietySittingCognitionPsychiatry

Résumé

récupéré en direct d'OpenAlex

I hesitate before asking, “Due to health or memory problems, do you have difficulty walking?” I can't stand for more than 15 minutes. I have five damaged vertebrae, and three are fused. Sometimes walking hurts so much I get dizzy. I need to stop and breathe. As Lawrence and I continue my questionnaire, other geriatric syndromes reveal themselves: urinary incontinence, frequent falls, anxiety, insomnia. His functional impairment, difficulty recalling personal contact information, and score on the Montreal Cognitive Assessment raise concern about mild cognitive impairment. His health profile is typical for a man in his eighth decade. Except Lawrence is only 57, he is wearing bright orange from head to toe, and we are sitting in a county jail. I spent the year between college and medical school getting to know older jail inmates like Lawrence. As a research assistant in the University of California at San Francisco Division of Geriatrics, I assisted in a project to assess and improve the health care of adults aged 55 and older who are leaving jail. My primary role was to listen to participants’ stories. As an aspiring geriatrician, I was curious to learn more about the medical needs of this rapidly growing, often unseen population. During our interview, Lawrence described some challenges of aging on the street. His family lives an hour outside the city, but he stays in San Francisco to comply with the terms of probation. He avoids shelters because “they're just not safe at my age. Even outside, you have a sleeping bag, the [younger ones] will take it.” Daily sources of stress include where to find a toilet so that he does not get in trouble for public urination and how to find his next meal. Lawrence cycles between correctional facilities and homelessness with frequency. These transitions make managing his health extremely challenging. He cannot always afford prescriptions and reports multiple hospitalizations within the last year—including one for heart failure exacerbation and a longer stay after a fall. With memory loss, he has trouble keeping regular medical appointments, and when his pain medication runs out, he turns to street-bought opioids. Recognizing his pressing situation, Lawrence's probation officer enlisted the help of a social worker to place him on a priority housing list for residents with serious health conditions. However, Lawrence was still homeless when I met him 6 months later—largely because he struggled to keep up with the required paperwork and appointments. Much of Lawrence's situation mirrors those of other participants; they remain in perpetual states of transition with shared medical and social difficulties. A struggle to fulfill basic needs. Community providers trying to connect clients with resources. Appointments and waiting lists. Complex medical issues. As an incoming medical student, piecing together these challenges was difficult. For a population with this many complex and interwoven challenges, what should high-quality care look like? A case manager specializing in dates. I ask him to clarify, and a new story unfolds. He was charged with petty theft 3 years ago but keeps forgetting his court date. He has been in and out of jail ever since for failing to appear. What would it take to keep Lawrence out of jail? A medical diagnosis of cognitive impairment, communication of that diagnosis to probation, compiling the necessary court documents, a case manager to ensure he does not miss his court date or medical appointments. I have memory loss. I am homeless. I have no calendar, no watch. I show up and they say, ‘Hey, man, your court date was yesterday.’ Then they arrest me again. The research study discussed is run by Dr. Brie Williams and Mr. Cyrus Ahalt at the Criminal Justice Health Project in the UCSF Division of Geriatrics. This manuscript was made possible by their support and mentorship. Subject name and other identifying features were altered slightly to protect confidentiality. Conflict of Interest: The editor in chief has reviewed the conflict of interest checklist provided by the author and has determined that the author has no financial or any other kind of personal conflicts with this paper. Author Contributions: Marielle Bolano conceptualized and wrote this manuscript. Sponsor's Role: None.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,002
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,186
Score d'incertitude au seuil0,623

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,002
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0060,001
Communication savante0,0030,003
Science ouverte0,0000,004
Intégrité de la recherche0,0010,003
Charge utile insuffisante (le modèle a refusé de juger)0,1860,060

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,035
Tête enseignante GPT0,334
Écart entre enseignants0,299 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2015
Routes d'admission1
Résumé présentoui

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