A Multicentric Field Test to Study the Validity and Feasibility of the SHS-tool to Screen for Serious Health-related Suffering in Adult Patients with Cancer
Notice bibliographique
Résumé
Objectives: The 2017 Lancet Commission reports ‘Serious Health-related Suffering’ (SHS) as an abyss in healthcare services. It lists 20 common health conditions and 15 symptoms as commonly associated with SHS. In 2015, 80% of SHS prevalence, an estimated 61 million, was noted as from low-middle-income countries. Acknowledging the high prevalence of SHS in cancer patients and aligning with global efforts to address and alleviate the suffering, the National Cancer Grid of India developed and evaluated the SHS screening tool (SHS-tool). The SHS tool was developed during phase 1 of the study through a systematic consensus-building methodology. During phase 2, the validity and feasibility study of the SHS tool was completed through a multicentric field test, which is described here. Materials and Methods: The SHS tool developed during phase 1 was field-tested across nine tertiary cancer care centres (TCC sites) selected from different healthcare sectors and regions of India. The study utilised a purposive sample of 254 cancer patients to evaluate the validity of the SHS screening tool at selected sites and additionally recorded the feasibility, relevance, acceptability and feedback comments from patients (n = 121), research associates (n = 11) and principal investigators (PIs) (n = 9). A documented interview of the patient within the same timeframe by experienced personnel selected by the PI served as the standard. Results: The field-test TCC-sites represented government academic institutions, non-government and private sectors. The sites used patient waiting areas and inpatient/daycare wards for conducting field tests. The Cronbach’s alpha of the SHS-tool questionnaire showed an internal consistency of 0.728. The tool detected SHS in 137/254 patients, compared to 116/254 through the interview method. The outcomes concurred with that of the interview in 64.17% of instances. The tool exhibited a sensitivity of 70% and specificity of 59%. 66.67% of patients might not have reached the interviewers if not for the field test processes. The feasibility questionnaire responses from patients (n = 121) indicated ease of understanding (91.74%), ease of use (92.56%) and relevance (89.26%). The selected settings were found suitable by 96.69%. Feedback responses from research associates indicated ease of administration (10/11) and relevance (8/11) and found no reasons preventing its use (8/11). The feedback comments from the stakeholders were thematically grouped for insights. Conclusion: The SHS tool is validated for screening SHS where none exists. It has been found to be a feasible, relevant and acceptable tool for use in adult cancer patients attending TCCs across India. Insights from analysing the feedback comments from the stakeholders have been integrated as ‘instruction for use’ for refined implementation of the SHS tool. The SHS tool may be utilised to recognise and trigger an in-depth evaluation and expedited access to essential palliative care packages towards alleviating it, as recommended by the Lancet Commission. Future studies using the SHS tool in other disease conditions with a high burden of SHS can assess its wider applicability.
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 | 0,066 | 0,072 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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 ».