Exploring Acceptability of Employment Interventions to Support People Living With Cancer: Qualitative Study of Cancer Survivors, Health Care Providers, and Employers
Notice bibliographique
Résumé
BACKGROUND: Employment contributes to cancer survivors' quality of life, but this population faces a variety of challenges when working during and after treatment. Factors associated with work outcomes among cancer survivors include disease and treatment status, work environment, and social support. While effective employment interventions have been developed in other clinical contexts, existing interventions have demonstrated inconsistent effectiveness in supporting cancer survivors at work. We conducted this study as a preliminary step toward program development for employment support among survivors at a rural comprehensive cancer center. OBJECTIVE: We aimed (1) to identify supports and resources that stakeholders (cancer survivors, health care providers, and employers) suggest may help cancer survivors to maintain employment and (2) to describe stakeholders' views on the advantages and disadvantages of intervention delivery models that incorporate those supports and resources. METHODS: We conducted a descriptive study collecting qualitative data from individual interviews and focus groups. Participants included adult cancer survivors, health care providers, and employers living or working in the Vermont-New Hampshire catchment area of the Dartmouth Cancer Center in Lebanon, New Hampshire. We grouped interview participants' recommended supports and resources into 4 intervention delivery models, which ranged on a continuum from less to more intensive to deliver. We then asked focus group participants to discuss the advantages and disadvantages of each of the 4 delivery models. RESULTS: Interview participants (n=45) included 23 cancer survivors, 17 health care providers, and 5 employers. Focus group participants (n=12) included 6 cancer survivors, 4 health care providers, and 2 employers. The four delivery models were (1) provision of educational materials, (2) individual consultation with cancer survivors, (3) joint consultation with both cancer survivors and their employers, and (4) peer support or advisory groups. Each participant type acknowledged the value of providing educational materials, which could be crafted to improve accommodation-related interactions between survivors and employers. Participants saw usefulness in individual consultation but expressed concern about the costs of program delivery and potential mismatches between consultant recommendations and the limits of what employers can provide. For joint consultation, employers liked being part of the solution and the possibility of enhanced communication. Potential drawbacks included additional logistical burden and its perceived generalizability to all types of workers and workplaces. Survivors and health care providers viewed the efficiency and potency of peer support as benefits of a peer advisory group but acknowledged the sensitivity of financial topics as a possible disadvantage of addressing work challenges in a group setting. CONCLUSIONS: The 3 participant groups identified both common and unique advantages and disadvantages of the 4 delivery models, reflecting varied barriers and facilitators to their potential implementation in practice. Theory-driven strategies to address implementation barriers should play a central role in further intervention development.
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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 | 0,023 | 0,034 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,010 | 0,007 |
| Communication savante | 0,003 | 0,004 |
| Science ouverte | 0,002 | 0,005 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».