Feasibility of Internet-Based Health-Related Quality of Life Data Collection in a Large Patient Cohort
Notice bibliographique
Résumé
BACKGROUND: Patient registries are commonly used to track survival and medical outcomes in large cohorts. However, large-scale collection of health-related quality of life (HRQOL) data is more challenging because such data must be collected directly from patients. Internet-based HRQOL questionnaires are a potential solution, allowing home data collection with immediate storage in a central database. OBJECTIVES: Our objectives were to investigate the sociodemographic predictors of Internet use and willingness to convey HRQOL information over the Internet in a Canadian tertiary care patient population and to determine whether Internet use patterns of tertiary care patients differ from those of the general Canadian population. Additionally, we sought to identify the success of home completion of Internet-based HRQOL questionnaires, as well as factors hindering home completion. METHODS: We surveyed 644 patients at the Toronto General and St. Michael's Hospitals from November 2003 through July 2006 within a prospective, longitudinal cohort study of HRQOL in patients with lung disease or lung transplants. Using multiple logistic regression, we assessed patient age, gender, rurality, marital status, and employment or education status as potential sociodemographic predictors of having an Internet-accessible home computer, using email at least weekly, and willingness to complete a quality of life questionnaire over the Internet. Patients electing to complete questionnaires over the Internet were followed from September 2005 through March 2008 to assess completion of HRQOL questionnaires from home, identify barriers for noncompletion, and determine sociodemographic predictors for home completion. RESULTS: Of the 644 patients, the median age was 51 years, with a similar number of males and females. Most were urban Ontario residents, were unemployed, and were married or in a common-law relationship. Having an Internet-accessible home computer was reported by 79.7% (513/644) of patients and use of email at least weekly by 66.5% (414/623) of patients. A majority of patients (57.1% 368/644) were willing to complete HRQOL questionnaires over the Internet via an emailed link. Of the participating 644 patients, 368 elected to complete future questionnaires from home and, as part of a gradual roll-out of the home HRQOL questionnaire, 211 were sent emails inviting them to do so. Of the invited patients, 78% (165/211) completed at least one questionnaire from home. The most common reason for noncompletion was a lack of or an inability to find time to complete the questionnaire. No statistically significant sociodemographic predictors of Internet use were associated with completion or noncompletion of questionnaires from home. CONCLUSIONS: Home, Internet-based HRQOL assessment is feasible in tertiary care patient populations with a high predicted rate of Internet usage based on sociodemographic parameters. A large minority of patients were unwilling or unable to take part in home HRQOL assessments indicating that alternative methods of data collection are still required. However, the majority of patients electing to complete home HRQOL assessments went on to do so over the Internet.
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 | 0,031 | 0,035 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| 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 ».