Feasibility of a child life specialist program for oncology patients with minor children at home: Demand and implementation.
Notice bibliographique
Résumé
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.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».