Submitting to the momentum of care: Processes of treatment decision making among older people with colorectal cancer
Notice bibliographique
Résumé
In Canada, 46% of new cancer cases and 64% of cancer deaths occur in people 70 years and older. Cancer treatment decision making among older people presents important challenges due to vast variations in health and functional status, changes in cancer pathology, changing social networks and resources, increased tension between quality and quantity of life, and lack of evidence to inform appropriate treatment recommendations. The complexity of this process, involving interactions among patients, healthcare providers, and family members that are shaped by the broader socio-political context, can cause distress to patients and those close to them. As the number of older Canadians is expected to more than double in the next 30 years, the need to optimize cancer treatment decision making for this group is paramount.Colorectal cancer is the second most common cancer diagnosis among older Canadians, with 53% of new cases occurring in men and women aged 70 years or older. With a 5-year net survival rate of 61% and complex trajectories involving multiple treatment modalities and burdensome side effects, colorectal cancer presents an important context within which to understand ongoing processes of treatment decision making among older people. This understanding is urgently needed to address salient age-based disparities and to offer optimal support. Purpose: To gain in-depth understanding of processes of treatment decision making from the perspective of people aged 70 years and older diagnosed with colorectal cancer. Methods: In this longitudinal, prospective, constructivist grounded theory study, participants included 18 people, aged 71 to 88 years, with an initial diagnosis of colon or rectal cancer, receiving care at a university-affiliated cancer centre in Montreal, Quebec, Canada. Participants were interviewed before and after their initial treatment. Between interviews (3 to 18 months), participants documented their thoughts and experiences in diary entries (written/self audio-recorded and/or through phone calls and brief visits). 35 interviews, 234 written or self-recorded diary entries, and 246 audio-recorded phone calls or brief visits with participants were generated. Medical information was collected from electronic health records. Interviews and relevant excerpts from diary entries were transcribed verbatim. Constant comparative methods of data analysis were used, relying on coding, questioning, and memo-writing. NVivo 10 software facilitated data management and analysis.Results: A dynamic, substantive theory of submitting to the momentum of care was constructed. This process began with an early decision point as participants stepped into the healthcare system, and then were swept into and through treatment. All actively worked, in their own ways, to align themselves with this momentum by situating self, managing self and system, and choosing to trust. If they were able to situate themselves and manage the momentum, they chose to trust and came to receive treatment by continuing to submit to the momentum of care. If they were unable, they lost trust and stepped away from the system. Paradoxes within participants’ accounts of decision making, and the role of age, information, existential perspectives, and identity, provided insights into the mechanisms underlying ongoing decision-making processes.Implications and Conclusions: Insights gained from this in-depth inquiry call us to re-examine existing models of treatment decision making. In this theory, cognitive, practical, and relational dimensions of cancer treatment decision making are integrated, highlighting the impact of contextual elements. Findings suggest the need for interventions that support trust relationships and enable older people with colorectal cancer to align themselves with the healthcare system. This theory provides a framework that may be used to inform clinical practice, education, policy, and future research
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,008 | 0,019 |
| 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 | 0,009 | 0,007 |
| Communication savante | 0,005 | 0,002 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».