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Record W2071188374 · doi:10.1080/15524256.2014.906375

Poverty and Pediatric Palliative Care: What Can We Do?

2014· article· en· W2071188374 on OpenAlexaff
Laura Beaune, Anne Leavens, Barbara Muskat, Lee Ford-Jones, Adam Rapoport, Randi Zlotnik Shaul, Julia Morinis, Lee Ann Chapman

Bibliographic record

VenueJournal of Social Work in End-of-Life & Palliative Care · 2014
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsAIDS Committee of TorontoUniversity of TorontoSickKids FoundationMount Sinai HospitalHospital for Sick Children
Fundersnot available
KeywordsPovertyHealth careNursingPalliative careSocioeconomic statusSocial workScope (computer science)PsychologyMedicinePopulationEnvironmental healthPolitical science

Abstract

fetched live from OpenAlex

It has been recognized that families of children with life-limiting health conditions struggle with significant financial demands, yet may not have awareness of resources available to them. Additionally, health care providers may not be aware of the socioeconomic needs of families they care for. This article describes a mixed-methods study examining the content validity and utility for health care providers of a poverty screening tool and companion resource guide for the pediatric palliative care population. The study found high relevance and validity of the tool. Significant barriers to implementing the screening tool in clinical practice were described by participants, including: concerns regarding time required, roles and responsibilities, and discomfort in asking about income. Implications for practice and suggestions for improving the tool are discussed. Screening and attention to the social determinants of health lie within the scope of practice of all health care providers. Social workers can play a leadership role in this work.

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)0.038
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.018
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.038
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0050.004
Scholarly communication0.0060.012
Open science0.0020.005
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.0040.000

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 head0.037
GPT teacher head0.340
Teacher spread0.302 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations16
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Social Work in End-of-Life & Palliative CareSame topicChildhood Cancer Survivors' Quality of LifeFrench-language works237,207