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Record W2782490736

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 on OpenAlexaboutno aff
Rachel Dunn

Bibliographic record

VenueNorthumbria Research Link (Northumbria University) · 2017
Typereview
Languageen
FieldSocial Sciences
TopicLegal Education and Practice Innovations
Canadian institutionsnot available
Fundersnot available
KeywordsMedical educationFrequently asked questionsPsychologyPublic relationsPolitical scienceMedicine
DOInot available

Abstract

fetched live from 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.065
metaresearch head score (Gemma)0.244
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.065
Threshold uncertainty score0.346

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0650.244
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.005
Bibliometrics0.0150.015
Science and technology studies0.0020.003
Scholarly communication0.0060.010
Open science0.0020.004
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0050.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.122
GPT teacher head0.466
Teacher spread0.344 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2017
Admission routes1
Has abstractyes

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