Bibliographic record
Abstract
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.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.186 | 0.060 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".