Using Social Media to Generate and Collect Primary Data: The #ShowsWorkplaceCompassion Twitter Research Campaign
Notice bibliographique
Résumé
BACKGROUND: Compassion is a core value embedded in the concept of quality in healthcare. The need for compassion toward healthcare staff in the workplace, for their own health and well-being and also to enable staff to deliver compassionate care for patients, is increasingly understood. However, we do not currently know how healthcare staff understand and characterize compassion toward themselves as opposed to patients. OBJECTIVE: The aim of this study was to use social media for the generation and collection of primary data to gain understanding of the concept of workplace compassion. METHODS: Tweets that contained the hashtag #ShowsWorkplaceCompassion were collected from Twitter and analyzed. The study took place between April 21 and May 21, 2016. Participants were self-selecting users of the social media service Twitter. The study was promoted by a number of routes: the National Health Service (NHS) England website, the personal Twitter accounts of the research team, internal NHS England communications, and via social media sharing. Participants were asked to contribute their views about what activities, actions, policies, philosophies or approaches demonstrate workplace compassion in healthcare using the hashtag #ShowsWorkplaceCompassion. All tweets including the research hashtag #ShowsWorkplaceCompassion were extracted from Twitter and studied using content analysis. Data concerning the frequency, nature, origin, and location of Web-based engagement with the research campaign were collected using Bitly (Bitly, Inc, USA) and Symplur (Symplur LLC, USA) software. RESULTS: A total of 260 tweets were analyzed. Of the 251 statements within the tweets that were coded, 37.8% (95/251) of the statements concerned Leadership and Management aspects of workplace compassion, 29.5% (74/251) were grouped under the theme related to Values and Culture, 17.5% (44/251) of the statements related to Personalized Policies and Procedures that support workplace compassion, and 15.2% (38/251) of the statements concerned Activities and Actions that show workplace compassion. Content analysis showed that small acts of kindness, an embedded organizational culture of caring for one another, and recognition of the emotional and physical impact of healthcare work were the most frequently mentioned characteristics of workplace compassion in healthcare. CONCLUSIONS: This study presents a new and innovative research approach using Twitter. Although previous research has analyzed the nature and pattern of tweets retrospectively, this study used Twitter to both recruit participants and collect primary data.
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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 | 0,005 | 0,019 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,012 | 0,009 |
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 ».