Methods and models in health-related quality of life (HRQOL) research: a state-of-the-art review
Notice bibliographique
Résumé
The 9th Annual Conference of the International Society for Quality of Life Research (ISOQOL) addressed state-ofthe-art methods and theoretical models for measuring health-related quality of life (HRQOL) and focused on opportunities for the use of these tools within practical settings and circumstances. The scientific program was chaired by Carolyn Schwartz (University of Massachusetts Medical School, MA, USA) and Jeff Sloan (Mayo Clinic, MN, USA). A variety of topics were addressed at the conference – chief among them were: • End-of-life issues • Chronic illness • The clinical significance and interpretation of research findings • Response shift • Adherence to therapy HRQOL at the end of life is a topic that does not ordinarily receive the attention it deserves. During the first plenary session, Tom Hack (CancerCare Manitoba, Canada) presented the results of a qualitative study through which the construct ‘dignity’ was operationalized and assessed. His approach to qualitative research, using innovative methodology undertaken with cancer patients receiving palliative care, was engaging and practical. At the same session, remarks provided by Carol Tishelman (Karolinska Institutet, Sweden) that focused on how to add precision to qualitative research were well received by the audience. The end-of-life issue received further attention at the conference during a special symposium chaired by Carolyn Schwartz. In the symposium, research was presented that ranged from new tools developed specifically for a seriously ill and/or dying population, to measuring the treatment preferences for those receiving end-of-life care. The end-of-life period and the issues raised by it are relevant for exploration by HRQOL research because the field has a natural relationship with chronic illness, regardless of whether the illness is in remission or is progressing. As is always the case at ISOQOL annual conferences, chronic illnesses of all sizes and shapes were fodder for HRQOL presentations. Topics that received special attention at one such session, chaired by Jane Scott (Mapi Values USA, NC, USA), included examination of models to accurately reflect the multidimensional nature of HRQOL assessment, using data from patients with AIDS, the psychological impact of cancer and treatment for it and how this relates to posttraumatic stress disorders, the relationship between HRQOL findings in patients with multiple sclerosis and other chronic neurological illnesses, HRQOL findings in cancer survivor patients and the interrelationships between pain, mental and physical health and HRQOL in people living with HIV infection. As the poster and oral research presented at the conference indicated, there is much interest in the development of more robust and precise instruments based on item banks and other state-of-the-art methodology. Computer-adapted testing, for example, has allowed researchers to develop instruments that require only a few items. However, due to the fact that each person’s response is dynamically matched to the best estimate of their level of health, these types of approaches result in shorter administration time, producing a final score that is practically identical in value and reliability to longer instruments which must be completed in their entirety to be effectively scored and interpreted. A debate regarding the pros and cons of computer-adaptive testing was moderated by David Osoba (QOL Consulting, Canada) during the conference. Three oral sessions were devoted to cancer. The papers presented and the issues discussed within one of these sessions included the use of QoL as a primary end-point within a lung cancer study. The paper, offered by Andrea Bezjak (Princess Margaret Hospital, Canada), was innovative because the successful use of HRQOL as a primary end-point was conducted within an international clinical study. Other noteworthy topics presented within this session, chaired by Galina Velikova (St James’s University Hospital, UK), included insights into:
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 | 0,068 | 0,116 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,003 |
| Méta-épidémiologie (sens large) | 0,006 | 0,006 |
| Bibliométrie | 0,017 | 0,027 |
| Études des sciences et des technologies | 0,002 | 0,008 |
| Communication savante | 0,011 | 0,016 |
| Science ouverte | 0,004 | 0,004 |
| Intégrité de la recherche | 0,005 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 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 ».