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What More do you Want?! A Systematic Review of the Literature Surrounding Knowledge, Skills and Attributes in Clinical Legal Education: What Regulators and Governing Bodies Asked Universities to do and How we Have Responded

2017· review· en· W2782490736 sur OpenAlexaboutno aff
Rachel Dunn

Notice bibliographique

RevueNorthumbria Research Link (Northumbria University) · 2017
Typereview
Langueen
DomaineSocial Sciences
ThématiqueLegal Education and Practice Innovations
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMedical educationFrequently asked questionsPsychologyPublic relationsPolitical scienceMedicine
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

This paper discusses part of my PhD research, which focuses on live client clinics and clinical legal education in Europe. My research aim for my literature review was to find all the literature available to me regarding clinical legal education and the knowledge, skills and attributes it can provide students with and analyse it. To do this I conducted a systematic review, searching various databases, using keywords and other searching techniques, to locate both qualitative, quantitative and conceptual research. A systematic review in this area has not been done previously. It has allowed me to bring together all the literature surrounding which knowledge, skills and attributes are necessary for the practice of law and how law schools have been working to meet these demands. Whilst my empirical research has been conducted within law schools in Europe, I wanted to look at the literature from a global perspective. Much influence to clinical legal education has come from US, Australia, UK and Canada and I thought it necessary to include these perspectives. \n \nThe systematic review did not only look at peer reviewed journals for data. The grey literature, which was mainly comprised of legal education reports from the UK, USA, Canada and Australia, was also sourced and analysed, bringing in another global perspective. Looking at this literature has enabled me to acknowledge what knowledge, skills and attributes legal education and practice regulators wish our students to graduate with and what is expected of legal educators. By comparing this with the peer reviewed literature found I have could analyse what it is that legal regulators want and how we, as legal educators, are responding. Are we meeting the demands laid down in policy? Is it even possible to meet these demands in a swiftly changing legal and economic climate? \n \nBy exploring the literature in this way, I could see the connections between what regulators want (grey literature) with how law schools have responded (qualitative, quantitative and conceptual literature). Law schools today use a variety of teaching methods, both experiential and not, to help better prepare our students for practice. However, these innovations seem to not be enough, and reports are continuously calling for law schools to further close the academic and practice divide. By tracking how law schools have responded to the demands of a changing legal world, I explore whether regulators and those with a vested interest in legal education are simply asking too much. \n \nThe epistemological framework which guided this research was mixed. When I was searching the literature I was very much using a positivist approach, following a strict methodology and deducing the results, moving from the general to specific reasoning. However, when I was synthesising and analysing the data I changed to a more interpretivist framework, exploring the literature, becoming more inductive, and moving from the specific to more general and broader theories. \n \nMethod \nThe systematic review followed the Cochrane Collaborative methodology, originally designed for use into medical research. The method followed included: \n \n-Mapping the field through a scoping review \n-Comprehensive search \n-Quality assessment \n-Data extraction \n-Synthesis \n-Write up. \n \nAll of these steps were recorded on a spreadsheet, allowing me to log which databases had been searched, the search results and how many articles I used from each search result. I then quality appraised all articles, giving them a score based on their content, extracted the data from them and synthesised the results. \n \nThe results will be presented in a PRISMA flow diagram, displaying how many peer reviewed articles I searched and the journey through which I selected those included in my literature review. This methodology claims to add more rigour to literature reviews, and to help eliminate the bias that can be associated with traditional, narrative reviews. \n \nAs this method was originally designed for quantitative studies it was imperative that I made some changes and adapted it for mixed methods systematic review. The stages of the method which are effected the most include the data extraction and synthesis. There is still no one established way to quality appraise and extract data from qualitative research, as there is with quantitative, within a systematic review. Thus, I used materials and influences from many different sources, including guidance provided by the UK Centre for Social Research. I also incorporated the Cochrane Qualitative Methods Group’s guidance on conducting quality appraisals, including their indicators of credibility, transferability, dependability and confirmability. Hopefully there will one day be a consensus of how to conduct mixed methods systematic reviews and a uniform methodology will be available. However, I believe that by incorporating different sources and influences I have established a good methodology for this research. \n \nThe synthesis and write up of this research was as visual as it was narrative. I used the aid of the visual to relay my findings and highlight the areas in which legal educators may not be focusing on within their curriculum, but which legal regulators and employers deem necessary for practice. However, by also incorporating conceptual papers and qualitative studies, I can discuss the reasons for the results in more depth, with the narrative complementing the numbers. \n \nExpected Outcomes \nI can see from the literature that law schools are attempting to respond to these reports. Legal education has undergone many changes, with a call for the gap between academia and practice to be less distinct. We can track the growth of law clinics throughout the world, which are designed to teach students the knowledge, skills and attributes needed for practice, bridge their academic knowledge to procedure and to install a social justice ethos for more responsible future lawyers. \n \nWe are developing our pedagogy in line with various reports, exploring and creating innovative ways to teach a holistic legal education, which can be seen in the peer reviewed literature. However, this still does not seem to be enough, and the issues surrounding the preparedness of those starting practice is continuously the burden of law schools globally. This begs the question of whether regulators are asking for too much from legal educators. Even where we respond to the reports and adapt our pedagogy, can we keep up with demands? Is it possible to provide the competent lawyers the market is demanding upon graduation? The findings of this systematic review will help to answer these questions, concluding that regulators are simply asking for too much that is not always realistically attainable. The link between policy and education is closer than one may think in legal education. This paper highlights the importance of a working relationship between them, for legal education to appropriately respond to the recommendations and for the recommendations to be reasonable. This relationship is not restricted by borders, and the similarities of what legal regulators wants from law schools is blatant. By working together and sharing our innovations legal education will continue to move forward and strive to meet the demands of the modern legal world.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,065
score de la tête « metaresearch » (Gemma)0,244
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Revue systématique · Signal consensuel: Revue systématique
GenreSignal candidat: Synthèse · Signal consensuel: Synthèse
Score de désaccord entre enseignants0,065
Score d'incertitude au seuil0,346

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0650,244
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0050,005
Bibliométrie0,0150,015
Études des sciences et des technologies0,0020,003
Communication savante0,0060,010
Science ouverte0,0020,004
Intégrité de la recherche0,0040,003
Charge utile insuffisante (le modèle a refusé de juger)0,0050,001

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.

Tête enseignante Opus0,122
Tête enseignante GPT0,466
Écart entre enseignants0,344 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeRevue systématique
Domainenon disponible
GenreSynthèse

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2017
Routes d'admission1
Résumé présentoui

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