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Feasibility of a child life specialist program for oncology patients with minor children at home: Demand and implementation.

2021· article· en· W3199894516 on OpenAlexaff
David L. Lysecki, Daryl Bainbridge, Tracy Akitt, Γεωργία Γεωργίου, Ralph M. Meyer, Jonathan Sussman

Bibliographic record

VenueJournal of Clinical Oncology · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicFamily Support in Illness
Canadian institutionsJuravinski Cancer CentreJuravinski HospitalMcMaster UniversityHamilton Health SciencesMcMaster Children's Hospital
Fundersnot available
KeywordsMedicineReferralPsychological interventionPediatricsMinor (academic)GrandparentFamily medicinePsychiatry

Abstract

fetched live from OpenAlex

28 Background: Up to 24% of adult oncology patients have minor children at home. Children may experience emotional problems, somatic complaints, social isolation, depression, and post-traumatic stress as a result. Typical support networks often fail to meet the needs of these families. To address this gap, an innovative Child Life Specialist (CLS) program for patients with minor children at home was offered at a tertiary oncology center. Methods: To understand the feasibility of this program, we examined the demand for and implementation of the CLS program over its initial 10 months. Demand was characterized using administrative data (referred patient/family demographics, referral details, and disease/treatment characteristics). Implementation was described through encounter data (audience, type of visit, interventions provided, time for preparation, and time of direct interaction for each encounter). Results: The program received 100 referrals, 93 of whom accessed the program. Patients were most often female (66%) with a median age of 45 years (range: 19 to 72). 81% were parents of minor children, 10% grandparents, and 9% other. Families predominantly had multiple children (98%), most commonly school-aged (ages 5-9, 39%; 10-14, 37%). 53% of families had two birth parents co-parenting in the same household; the remainder had alternate parent/living scenarios. Most referrals came from social work (57%). Median time from diagnosis to referral was 79 days (range: 9d-6.5y). Breast cancer (26%) was the most common diagnosis, followed by gastrointestinal (19%) and hematologic (16%). Cancer phase at referral was defined as at new diagnosis (within 30d, 18%), undergoing treatment with curative intent (20%), undergoing treatment with palliative intent (39%), at end of life (within 30d, 16%) and after death/bereavement (5%). 1 patient (1%) did not have cancer. The CLS recorded 257 unique encounters. 55% of encounters included patients, 40% non-patient parents, 21% children, and 21% others. 75% were individual encounters, while 25% were group encounters. 95% of encounters that included children also included an adult. Phone calls were the most frequent encounter type (43%), but hospital visits consumed the largest proportion of recorded CLS time (38%). Mean encounter time (all visit types) included 20min for preparation and 51min of direct interaction. CLS interventions included: guidance on talking with children (67% of encounters), providing resources (37%), diagnostic teaching (21%), end-of-life support (18%), discussing change in status (10%), grief (8%), and emotional expression (4%). Conclusions: This study characterized the demand for this program and described its implementation over the pilot period. This period occurred during the Covid-19 pandemic, which dramatically altered healthcare and family visitation, likely influencing the results of this study.

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)0.004
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
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 head0.089
GPT teacher head0.485
Teacher spread0.396 · 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 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".

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Citations1
Published2021
Admission routes1
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